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目标检测

Object Detection(目标检测神文)

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目標檢測神文，非常全而且持續在更新。轉發自：https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html，如有侵權聯系刪除。
更新時間：
20181026

我會跟進原作者博客持續更新，加入自己對目標檢測領域的一些新研究及論文解讀。博客根據需求直接進行關鍵字搜索，例如2018，可找到最新論文。

文章目錄

Papers
- - Deep Neural Networks for Object Detection
  - OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
- R-CNN
  - Rich feature hierarchies for accurate object detection and semantic segmentation
- Fast R-CNN
  - Fast R-CNN
  - A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
- Faster R-CNN
  - Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
  - R-CNN minus R
  - Faster R-CNN in MXNet with distributed implementation and data parallelization
  - Contextual Priming and Feedback for Faster R-CNN
  - An Implementation of Faster RCNN with Study for Region Sampling
  - Interpretable R-CNN
- Light-Head R-CNN
  - Light-Head R-CNN: In Defense of Two-Stage Object Detector
  - Cascade R-CNN: Delving into High Quality Object Detection
- MultiBox
  - Scalable Object Detection using Deep Neural Networks
  - Scalable, High-Quality Object Detection
- SPP-Net
  - Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
  - DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
  - Object Detectors Emerge in Deep Scene CNNs
  - segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
  - Object Detection Networks on Convolutional Feature Maps
  - Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
  - DeepBox: Learning Objectness with Convolutional Networks
- MR-CNN
  - Object detection via a multi-region & semantic segmentation-aware CNN model
- YOLO
  - You Only Look Once: Unified, Real-Time Object Detection
  - darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
  - Start Training YOLO with Our Own Data
  - YOLO: Core ML versus MPSNNGraph
  - TensorFlow YOLO object detection on Android
  - Computer Vision in iOS – Object Detection
- YOLOv2
  - YOLO9000: Better, Faster, Stronger
  - darknet_scripts
  - Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
  - LightNet: Bringing pjreddie’s DarkNet out of the shadows
  - YOLO v2 Bounding Box Tool
- YOLOv3
  - YOLOv3: An Incremental Improvement
  - AttentionNet: Aggregating Weak Directions for Accurate Object Detection
- DenseBox
  - DenseBox: Unifying Landmark Localization with End to End Object Detection
- SSD
  - SSD: Single Shot MultiBox Detector
- DSSD
  - DSSD : Deconvolutional Single Shot Detector
  - Enhancement of SSD by concatenating feature maps for object detection
  - Context-aware Single-Shot Detector
  - Feature-Fused SSD: Fast Detection for Small Objects
- FSSD
  - FSSD: Feature Fusion Single Shot Multibox Detector
  - Weaving Multi-scale Context for Single Shot Detector
- ESSD
  - Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
  - Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
  - MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects
- Inside-Outside Net (ION)
  - Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
  - Adaptive Object Detection Using Adjacency and Zoom Prediction
  - G-CNN: an Iterative Grid Based Object Detector
- Factors in Finetuning Deep Model for object detection
  - Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution
  - We don’t need no bounding-boxes: Training object class detectors using only human verification
  - HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
  - A MultiPath Network for Object Detection
- CRAFT
  - CRAFT Objects from Images
- OHEM
  - Training Region-based Object Detectors with Online Hard Example Mining
  - S-OHEM: Stratified Online Hard Example Mining for Object Detection
  - Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
- R-FCN
  - R-FCN: Object Detection via Region-based Fully Convolutional Networks
  - R-FCN-3000 at 30fps: Decoupling Detection and Classification
  - Recycle deep features for better object detection
- MS-CNN
  - A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
  - Multi-stage Object Detection with Group Recursive Learning
  - Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
- PVANET
  - PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
- GBD-Net
  - Gated Bi-directional CNN for Object Detection
  - Crafting GBD-Net for Object Detection
  - StuffNet: Using ‘Stuff’ to Improve Object Detection
  - Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
  - Hierarchical Object Detection with Deep Reinforcement Learning
  - Learning to detect and localize many objects from few examples
  - Speed/accuracy trade-offs for modern convolutional object detectors
  - SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
- Feature Pyramid Network (FPN)
  - Feature Pyramid Networks for Object Detection
  - Action-Driven Object Detection with Top-Down Visual Attentions
  - Beyond Skip Connections: Top-Down Modulation for Object Detection
  - Wide-Residual-Inception Networks for Real-time Object Detection
  - Attentional Network for Visual Object Detection
  - Learning Chained Deep Features and Classifiers for Cascade in Object Detection
  - DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
  - Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
  - Spatial Memory for Context Reasoning in Object Detection
  - Accurate Single Stage Detector Using Recurrent Rolling Convolution
  - Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
  - LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
  - Point Linking Network for Object Detection
  - Perceptual Generative Adversarial Networks for Small Object Detection
  - Few-shot Object Detection
  - Yes-Net: An effective Detector Based on Global Information
  - SMC Faster R-CNN: Toward a scene-specialized multi-object detector
  - Towards lightweight convolutional neural networks for object detection
  - RON: Reverse Connection with Objectness Prior Networks for Object Detection
  - Mimicking Very Efficient Network for Object Detection
  - Residual Features and Unified Prediction Network for Single Stage Detection
  - Deformable Part-based Fully Convolutional Network for Object Detection
  - Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
  - Recurrent Scale Approximation for Object Detection in CNN
- DSOD
  - DSOD: Learning Deeply Supervised Object Detectors from Scratch
  - Object Detection from Scratch with Deep Supervision
  - Focal Loss for Dense Object Detection
  - Focal Loss Dense Detector for Vehicle Surveillance
  - CoupleNet: Coupling Global Structure with Local Parts for Object Detection
  - Incremental Learning of Object Detectors without Catastrophic Forgetting
  - Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
  - StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
  - Dynamic Zoom-in Network for Fast Object Detection in Large Images
  - Zero-Annotation Object Detection with Web Knowledge Transfer
- MegDet
  - MegDet: A Large Mini-Batch Object Detector
  - Single-Shot Refinement Neural Network for Object Detection
  - Receptive Field Block Net for Accurate and Fast Object Detection
  - An Analysis of Scale Invariance in Object Detection - SNIP
  - Feature Selective Networks for Object Detection
  - Learning a Rotation Invariant Detector with Rotatable Bounding Box
  - Scalable Object Detection for Stylized Objects
  - Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
  - Deep Regionlets for Object Detection
  - Training and Testing Object Detectors with Virtual Images
  - Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
  - Spot the Difference by Object Detection
  - Localization-Aware Active Learning for Object Detection
  - Object Detection with Mask-based Feature Encoding
  - LSTD: A Low-Shot Transfer Detector for Object Detection
  - Domain Adaptive Faster R-CNN for Object Detection in the Wild
  - Pseudo Mask Augmented Object Detection
  - Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
  - Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
  - Learning Region Features for Object Detection
  - Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
  - Object Detection for Comics using Manga109 Annotations
  - Task-Driven Super Resolution: Object Detection in Low-resolution Images
  - Transferring Common-Sense Knowledge for Object Detection
  - Multi-scale Location-aware Kernel Representation for Object Detection
  - Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
  - DetNet: A Backbone network for Object Detection
  - Robust Physical Adversarial Attack on Faster R-CNN Object Detector
  - AdvDetPatch: Attacking Object Detectors with Adversarial Patches
  - Attacking Object Detectors via Imperceptible Patches on Background
  - Physical Adversarial Examples for Object Detectors
  - Quantization Mimic: Towards Very Tiny CNN for Object Detection
  - Object detection at 200 Frames Per Second
  - Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images
  - SNIPER: Efficient Multi-Scale Training
  - Soft Sampling for Robust Object Detection
  - MetaAnchor: Learning to Detect Objects with Customized Anchors
  - Localization Recall Precision (LRP): A New Performance Metric for Object Detection
  - Auto-Context R-CNN
  - Pooling Pyramid Network for Object Detection
  - Modeling Visual Context is Key to Augmenting Object Detection Datasets
  - Dual Refinement Network for Single-Shot Object Detection
  - Acquisition of Localization Confidence for Accurate Object Detection
  - CornerNet: Detecting Objects as Paired Keypoints
  - Unsupervised Hard Example Mining from Videos for Improved Object Detection
  - SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection
  - A Survey of Modern Object Detection Literature using Deep Learning
  - Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
  - Deep Feature Pyramid Reconfiguration for Object Detection
  - MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection
  - Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
  - Deep Learning for Generic Object Detection: A Survey
- Non-Maximum Suppression (NMS)
  - End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
  - A convnet for non-maximum suppression
  - Soft-NMS – Improving Object Detection With One Line of Code
  - Learning non-maximum suppression
  - Relation Networks for Object Detection
- Adversarial Examples
  - Adversarial Examples that Fool Detectors
  - Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
- Weakly Supervised Object Detection
  - Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
  - Weakly supervised object detection using pseudo-strong labels
  - Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
  - Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
- Video Object Detection
  - Learning Object Class Detectors from Weakly Annotated Video
  - Analysing domain shift factors between videos and images for object detection
  - Video Object Recognition
  - Deep Learning for Saliency Prediction in Natural Video
  - T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
  - Object Detection from Video Tubelets with Convolutional Neural Networks
  - Object Detection in Videos with Tubelets and Multi-context Cues
  - Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
  - CNN Based Object Detection in Large Video Images
  - Object Detection in Videos with Tubelet Proposal Networks
  - Flow-Guided Feature Aggregation for Video Object Detection
  - Video Object Detection using Faster R-CNN
  - Improving Context Modeling for Video Object Detection and Tracking
  - Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
  - Mobile Video Object Detection with Temporally-Aware Feature Maps
  - Towards High Performance Video Object Detection
  - Impression Network for Video Object Detection
  - Spatial-Temporal Memory Networks for Video Object Detection
  - 3D-DETNet: a Single Stage Video-Based Vehicle Detector
  - Object Detection in Videos by Short and Long Range Object Linking
  - Object Detection in Video with Spatiotemporal Sampling Networks
  - Towards High Performance Video Object Detection for Mobiles
  - Optimizing Video Object Detection via a Scale-Time Lattice
  - Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing
- Object Detection on Mobile Devices
  - Pelee: A Real-Time Object Detection System on Mobile Devices
- Object Detection in 3D
  - Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
  - Complex-YOLO: Real-time 3D Object Detection on Point Clouds
  - Focal Loss in 3D Object Detection
- Object Detection on RGB-D
  - Learning Rich Features from RGB-D Images for Object Detection and Segmentation
  - Differential Geometry Boosts Convolutional Neural Networks for Object Detection
  - A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
- Zero-Shot Object Detection
  - Zero-Shot Detection
  - Zero-Shot Object Detection
  - Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
  - Zero-Shot Object Detection by Hybrid Region Embedding
- Salient Object Detection
  - Best Deep Saliency Detection Models (CVPR 2016 & 2015)
  - Large-scale optimization of hierarchical features for saliency prediction in natural images
  - Predicting Eye Fixations using Convolutional Neural Networks
  - Saliency Detection by Multi-Context Deep Learning
  - DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
  - SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection
  - Shallow and Deep Convolutional Networks for Saliency Prediction
  - Recurrent Attentional Networks for Saliency Detection
  - Two-Stream Convolutional Networks for Dynamic Saliency Prediction
- Unconstrained Salient Object Detection
  - Unconstrained Salient Object Detection via Proposal Subset Optimization
  - DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection
  - Salient Object Subitizing
  - Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection
  - Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs
  - Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
  - A Deep Multi-Level Network for Saliency Prediction
  - Visual Saliency Detection Based on Multiscale Deep CNN Features
  - A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
  - Deeply supervised salient object detection with short connections
  - Weakly Supervised Top-down Salient Object Detection
  - SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
  - Visual Saliency Prediction Using a Mixture of Deep Neural Networks
  - A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network
  - Saliency Detection by Forward and Backward Cues in Deep-CNNs
  - Supervised Adversarial Networks for Image Saliency Detection
  - Group-wise Deep Co-saliency Detection
  - Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
  - Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
  - Learning Uncertain Convolutional Features for Accurate Saliency Detection
  - Deep Edge-Aware Saliency Detection
  - Self-explanatory Deep Salient Object Detection
  - PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection
  - DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets
  - Recurrently Aggregating Deep Features for Salient Object Detection
  - Deep saliency: What is learnt by a deep network about saliency?
  - Contrast-Oriented Deep Neural Networks for Salient Object Detection
  - Salient Object Detection by Lossless Feature Reflection
  - HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection
- Video Saliency Detection
  - Deep Learning For Video Saliency Detection
  - Video Salient Object Detection Using Spatiotemporal Deep Features
  - Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM
- Visual Relationship Detection
  - Visual Relationship Detection with Language Priors
  - ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection
  - Visual Translation Embedding Network for Visual Relation Detection
  - Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection
  - Detecting Visual Relationships with Deep Relational Networks
  - Identifying Spatial Relations in Images using Convolutional Neural Networks
  - PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
  - Natural Language Guided Visual Relationship Detection
  - Detecting Visual Relationships Using Box Attention
  - Google AI Open Images - Visual Relationship Track
  - Context-Dependent Diffusion Network for Visual Relationship Detection
  - A Problem Reduction Approach for Visual Relationships Detection
- Face Deteciton
  - Multi-view Face Detection Using Deep Convolutional Neural Networks
  - From Facial Parts Responses to Face Detection: A Deep Learning Approach
  - Compact Convolutional Neural Network Cascade for Face Detection
  - Face Detection with End-to-End Integration of a ConvNet and a 3D Model
  - CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
  - Towards a Deep Learning Framework for Unconstrained Face Detection
  - Supervised Transformer Network for Efficient Face Detection
  - UnitBox: An Advanced Object Detection Network
  - Bootstrapping Face Detection with Hard Negative Examples
  - Grid Loss: Detecting Occluded Faces
  - A Multi-Scale Cascade Fully Convolutional Network Face Detector
- MTCNN
  - Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks
  - Face Detection using Deep Learning: An Improved Faster RCNN Approach
  - Faceness-Net: Face Detection through Deep Facial Part Responses
  - Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”
  - End-To-End Face Detection and Recognition
  - Face R-CNN
  - Face Detection through Scale-Friendly Deep Convolutional Networks
  - Scale-Aware Face Detection
  - Detecting Faces Using Inside Cascaded Contextual CNN
  - Multi-Branch Fully Convolutional Network for Face Detection
  - SSH: Single Stage Headless Face Detector
  - Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container
  - FaceBoxes: A CPU Real-time Face Detector with High Accuracy
  - S3FD: Single Shot Scale-invariant Face Detector
  - Detecting Faces Using Region-based Fully Convolutional Networks
  - AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
  - Face Attention Network: An effective Face Detector for the Occluded Faces
  - Feature Agglomeration Networks for Single Stage Face Detection
  - Face Detection Using Improved Faster RCNN
  - PyramidBox: A Context-assisted Single Shot Face Detector
  - A Fast Face Detection Method via Convolutional Neural Network
  - Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
  - Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
  - SFace: An Efficient Network for Face Detection in Large Scale Variations
  - Survey of Face Detection on Low-quality Images
  - Anchor Cascade for Efficient Face Detection
  - Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization
  - Selective Refinement Network for High Performance Face Detection
- Detect Small Faces
  - Finding Tiny Faces
  - Detecting and counting tiny faces
  - Seeing Small Faces from Robust Anchor’s Perspective
  - Face-MagNet: Magnifying Feature Maps to Detect Small Faces
- Person Head Detection
  - Context-aware CNNs for person head detection
  - Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture
  - A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications
  - FCHD: A fast and accurate head detector
- Pedestrian Detection / People Detection
  - Pedestrian Detection aided by Deep Learning Semantic Tasks
  - Deep Learning Strong Parts for Pedestrian Detection
  - Taking a Deeper Look at Pedestrians
  - Convolutional Channel Features
  - End-to-end people detection in crowded scenes
  - Learning Complexity-Aware Cascades for Deep Pedestrian Detection
  - Deep convolutional neural networks for pedestrian detection
  - Scale-aware Fast R-CNN for Pedestrian Detection
  - New algorithm improves speed and accuracy of pedestrian detection
  - Pushing the Limits of Deep CNNs for Pedestrian Detection
  - A Real-Time Deep Learning Pedestrian Detector for Robot Navigation
  - A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation
  - Is Faster R-CNN Doing Well for Pedestrian Detection?
  - Unsupervised Deep Domain Adaptation for Pedestrian Detection
  - Reduced Memory Region Based Deep Convolutional Neural Network Detection
  - Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
  - Detecting People in Artwork with CNNs
  - Multispectral Deep Neural Networks for Pedestrian Detection
  - Deep Multi-camera People Detection
  - Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
  - What Can Help Pedestrian Detection?
  - Illuminating Pedestrians via Simultaneous Detection & Segmentation
  - Rotational Rectification Network for Robust Pedestrian Detection
  - STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos
  - Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy
  - Repulsion Loss: Detecting Pedestrians in a Crowd
  - Aggregated Channels Network for Real-Time Pedestrian Detection
  - Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
  - Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection
  - Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
  - PCN: Part and Context Information for Pedestrian Detection with CNNs
  - Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
  - Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
  - Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
- Vehicle Detection
  - DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
  - Evolving Boxes for fast Vehicle Detection
  - Fine-Grained Car Detection for Visual Census Estimation
  - SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
  - Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
- Traffic-Sign Detection
  - Traffic-Sign Detection and Classification in the Wild
  - Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data
  - Detecting Small Signs from Large Images
  - Localized Traffic Sign Detection with Multi-scale Deconvolution Networks
  - Detecting Traffic Lights by Single Shot Detection
  - A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
- Skeleton Detection
  - Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
  - DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
  - SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
  - Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
- Fruit Detection
  - Deep Fruit Detection in Orchards
  - Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
- Shadow Detection
  - Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
  - A+D-Net: Shadow Detection with Adversarial Shadow Attenuation
  - Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
  - Direction-aware Spatial Context Features for Shadow Detection
  - Direction-aware Spatial Context Features for Shadow Detection and Removal
- Others Detection
  - Deep Deformation Network for Object Landmark Localization
  - Fashion Landmark Detection in the Wild
  - Deep Learning for Fast and Accurate Fashion Item Detection
  - OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)
  - Selfie Detection by Synergy-Constraint Based Convolutional Neural Network
  - Associative Embedding:End-to-End Learning for Joint Detection and Grouping
  - Deep Cuboid Detection: Beyond 2D Bounding Boxes
  - Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
  - Deep Learning Logo Detection with Data Expansion by Synthesising Context
  - Scalable Deep Learning Logo Detection
  - Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks
  - Automatic Handgun Detection Alarm in Videos Using Deep Learning
  - Objects as context for part detection
  - Using Deep Networks for Drone Detection
  - Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
  - Target Driven Instance Detection
  - DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion
  - VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
  - Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants
  - ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos
  - Deep Learning Object Detection Methods for Ecological Camera Trap Data
  - EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
  - Towards End-to-End Lane Detection: an Instance Segmentation Approach
  - iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
  - Densely Supervised Grasp Detector (DSGD)
- Object Proposal
  - DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
  - Scale-aware Pixel-wise Object Proposal Networks
  - Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
  - Learning to Segment Object Proposals via Recursive Neural Networks
  - Learning Detection with Diverse Proposals
  - ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
  - Improving Small Object Proposals for Company Logo Detection
  - Open Logo Detection Challenge
- Localization
  - Beyond Bounding Boxes: Precise Localization of Objects in Images
  - Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
  - Weakly Supervised Object Localization Using Size Estimates
  - Active Object Localization with Deep Reinforcement Learning
  - Localizing objects using referring expressions
  - LocNet: Improving Localization Accuracy for Object Detection
  - Learning Deep Features for Discriminative Localization
  - ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
  - Ensemble of Part Detectors for Simultaneous Classification and Localization
  - STNet: Selective Tuning of Convolutional Networks for Object Localization
  - Soft Proposal Networks for Weakly Supervised Object Localization
  - Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
- Tutorials / Talks
  - Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection
  - Towards Good Practices for Recognition & Detection
  - Work in progress: Improving object detection and instance segmentation for small objects
  - Object Detection with Deep Learning: A Review
- Projects
  - Detectron
  - TensorBox: a simple framework for training neural networks to detect objects in images
  - Object detection in torch: Implementation of some object detection frameworks in torch
  - Using DIGITS to train an Object Detection network
  - FCN-MultiBox Detector
  - KittiBox: A car detection model implemented in Tensorflow.
  - Deformable Convolutional Networks + MST + Soft-NMS
  - How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow
  - Metrics for object detection
- Leaderboard
  - Detection Results: VOC2012
- Tools
  - BeaverDam: Video annotation tool for deep learning training labels
- Blogs
  - Convolutional Neural Networks for Object Detection
  - Introducing automatic object detection to visual search (Pinterest)
  - Deep Learning for Object Detection with DIGITS
  - Analyzing The Papers Behind Facebook’s Computer Vision Approach
  - Easily Create High Quality Object Detectors with Deep Learning
  - How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit
  - Object Detection in Satellite Imagery, a Low Overhead Approach
  - You Only Look Twice?—?Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks
  - Faster R-CNN Pedestrian and Car Detection
  - Small U-Net for vehicle detection
  - Region of interest pooling explained
  - Supercharge your Computer Vision models with the TensorFlow Object Detection API
  - Understanding SSD MultiBox?—?Real-Time Object Detection In Deep Learning
  - One-shot object detection
  - An overview of object detection: one-stage methods

Methodbackbonetest sizeVOC2007VOC2010VOC2012ILSVRC 2013MSCOCO 2015Speed

OverFeat	?	?	?	?	?	24.3%	?	?
R-CNN	AlexNet	?	58.5%	53.7%	53.3%	31.4%	?	?
R-CNN	VGG17	?	66.0%	?	?	?	?	?
SPP_net	ZF-5	?	54.2%	?	?	31.84%	?	?
DeepID-Net	?	?	64.1%	?	?	50.3%	?	?
NoC	?	?	73.3%	?	68.8%	?	?	?
Fast-RCNN	VGG16	?	70.0%	68.8%	68.4%	?	19.7%(@[0.5-0.95]), 35.9%(@0.5)	?
MR-CNN	?	?	78.2%	?	73.9%	?	?	?
Faster-RCNN	VGG16	?	78.8%	?	75.9%	?	21.9%(@[0.5-0.95]), 42.7%(@0.5)	198ms
Faster-RCNN	ResNet101	?	85.6%	?	83.8%	?	37.4%(@[0.5-0.95]), 59.0%(@0.5)	?
YOLO	?	?	63.4%	?	57.9%	?	?	45 fps
YOLO	VGG-16	?	66.4%	?	?	?	?	21 fps
YOLOv2	?	448x448	78.6%	?	73.4%	?	21.6%(@[0.5-0.95]), 44.0%(@0.5)	40 fps
SSD	VGG16	300x300	77.2%	?	75.8%	?	25.1%(@[0.5-0.95]), 43.1%(@0.5)	46 fps
SSD	VGG16	512x512	79.8%	?	78.5%	?	28.8%(@[0.5-0.95]), 48.5%(@0.5)	19 fps
SSD	ResNet101	300x300	?	?	?	?	28.0%(@[0.5-0.95])	16 fps
SSD	ResNet101	512x512	?	?	?	?	31.2%(@[0.5-0.95])	8 fps
DSSD	ResNet101	300x300	?	?	?	?	28.0%(@[0.5-0.95])	8 fps
DSSD	ResNet101	500x500	?	?	?	?	33.2%(@[0.5-0.95])	6 fps
ION	?	?	79.2%	?	76.4%	?	?	?
CRAFT	?	?	75.7%	?	71.3%	48.5%	?	?
OHEM	?	?	78.9%	?	76.3%	?	25.5%(@[0.5-0.95]), 45.9%(@0.5)	?
R-FCN	ResNet50	?	77.4%	?	?	?	?	0.12sec(K40), 0.09sec(TitianX)
R-FCN	ResNet101	?	79.5%	?	?	?	?	0.17sec(K40), 0.12sec(TitianX)
R-FCN(ms train)	ResNet101	?	83.6%	?	82.0%	?	31.5%(@[0.5-0.95]), 53.2%(@0.5)	?
PVANet 9.0	?	?	84.9%	?	84.2%	?	?	750ms(CPU), 46ms(TitianX)
RetinaNet	ResNet101-FPN	?	?	?	?	?	?	?
Light-Head R-CNN	Xception*	800/1200	?	?	?	?	31.5%@[0.5:0.95]	95 fps
Light-Head R-CNN	Xception*	700/1100	?	?	?	?	30.7%@[0.5:0.95]	102 fps

Papers

Deep Neural Networks for Object Detection

paper:?http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

arxiv:?http://arxiv.org/abs/1312.6229
github:?https://github.com/sermanet/OverFeat
code:?http://cilvr.nyu.edu/doku.php?id=software:overfeat:start

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

intro: R-CNN
arxiv:?http://arxiv.org/abs/1311.2524
supp:?http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
slides:?http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
slides:?http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
github:?https://github.com/rbgirshick/rcnn
notes:?http://zhangliliang.com/2014/07/23/paper-note-rcnn/
caffe-pr(“Make R-CNN the Caffe detection example”):?https://github.com/BVLC/caffe/pull/482

Fast R-CNN

arxiv:?http://arxiv.org/abs/1504.08083
slides:?http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
github:?https://github.com/rbgirshick/fast-rcnn
github(COCO-branch):?https://github.com/rbgirshick/fast-rcnn/tree/coco
webcam demo:?https://github.com/rbgirshick/fast-rcnn/pull/29
notes:?http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
notes:?http://blog.csdn.net/linj_m/article/details/48930179
github(“Fast R-CNN in MXNet”):?https://github.com/precedenceguo/mx-rcnn
github:?https://github.com/mahyarnajibi/fast-rcnn-torch
github:?https://github.com/apple2373/chainer-simple-fast-rnn
github:?https://github.com/zplizzi/tensorflow-fast-rcnn

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

intro: CVPR 2017
arxiv:?https://arxiv.org/abs/1704.03414
paper:?http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf
github(Caffe):?https://github.com/xiaolonw/adversarial-frcnn

Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

intro: NIPS 2015
arxiv:?http://arxiv.org/abs/1506.01497
gitxiv:?http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
slides:?http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
github(official, Matlab):?https://github.com/ShaoqingRen/faster_rcnn
github:?https://github.com/rbgirshick/py-faster-rcnn
github(MXNet):?https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn
github:?https://github.com//jwyang/faster-rcnn.pytorch
github:?https://github.com/mitmul/chainer-faster-rcnn
github:?https://github.com/andreaskoepf/faster-rcnn.torch
github:?https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
github:?https://github.com/smallcorgi/Faster-RCNN_TF
github:?https://github.com/CharlesShang/TFFRCNN
github(C++ demo):?https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
github:?https://github.com/yhenon/keras-frcnn
github:?https://github.com/Eniac-Xie/faster-rcnn-resnet
github(C++):?https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev

R-CNN minus R

intro: BMVC 2015
arxiv:?http://arxiv.org/abs/1506.06981

Faster R-CNN in MXNet with distributed implementation and data parallelization

github:?https://github.com/dmlc/mxnet/tree/master/example/rcnn

Contextual Priming and Feedback for Faster R-CNN

intro: ECCV 2016. Carnegie Mellon University
paper:?http://abhinavsh.info/context_priming_feedback.pdf
poster:?http://www.eccv2016.org/files/posters/P-1A-20.pdf

An Implementation of Faster RCNN with Study for Region Sampling

intro: Technical Report, 3 pages. CMU
arxiv:?https://arxiv.org/abs/1702.02138
github:?https://github.com/endernewton/tf-faster-rcnn

Interpretable R-CNN

intro: North Carolina State University & Alibaba
keywords: AND-OR Graph (AOG)
arxiv:?https://arxiv.org/abs/1711.05226

Light-Head R-CNN

Light-Head R-CNN: In Defense of Two-Stage Object Detector

intro: Tsinghua University & Megvii Inc
arxiv:?https://arxiv.org/abs/1711.07264
github(official, Tensorflow):?https://github.com/zengarden/light_head_rcnn
github:?https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet_v1_101_rfcn_light.py#L784

##Cascade R-CNN

Cascade R-CNN: Delving into High Quality Object Detection

intro: CVPR 2018. UC San Diego
arxiv:?https://arxiv.org/abs/1712.00726
github(Caffe, official):?https://github.com/zhaoweicai/cascade-rcnn

MultiBox

Scalable Object Detection using Deep Neural Networks

intro: first MultiBox. Train a CNN to predict Region of Interest.
arxiv:?http://arxiv.org/abs/1312.2249
github:?https://github.com/google/multibox
blog:?https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html

Scalable, High-Quality Object Detection

intro: second MultiBox
arxiv:?http://arxiv.org/abs/1412.1441
github:?https://github.com/google/multibox

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

intro: ECCV 2014 / TPAMI 2015
arxiv:?http://arxiv.org/abs/1406.4729
github:?https://github.com/ShaoqingRen/SPP_net
notes:?http://zhangliliang.com/2014/09/13/paper-note-sppnet/

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

intro: PAMI 2016
intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
project page:?http://www.ee.cuhk.edu.hk/?wlouyang/projects/imagenetDeepId/index.html
arxiv:?http://arxiv.org/abs/1412.5661

Object Detectors Emerge in Deep Scene CNNs

intro: ICLR 2015
arxiv:?http://arxiv.org/abs/1412.6856
paper:?https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf
paper:?https://people.csail.mit.edu/khosla/papers/iclr2015_zhou.pdf
slides:?http://places.csail.mit.edu/slide_iclr2015.pdf

segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

intro: CVPR 2015
project(code+data):?https://www.cs.toronto.edu/~yukun/segdeepm.html
arxiv:?https://arxiv.org/abs/1502.04275
github:?https://github.com/YknZhu/segDeepM

Object Detection Networks on Convolutional Feature Maps

intro: TPAMI 2015
keywords: NoC
arxiv:?http://arxiv.org/abs/1504.06066

Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

arxiv:?http://arxiv.org/abs/1504.03293
slides:?http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf
github:?https://github.com/YutingZhang/fgs-obj

DeepBox: Learning Objectness with Convolutional Networks

keywords: DeepBox
arxiv:?http://arxiv.org/abs/1505.02146
github:?https://github.com/weichengkuo/DeepBox

MR-CNN

Object detection via a multi-region & semantic segmentation-aware CNN model

intro: ICCV 2015. MR-CNN
arxiv:?http://arxiv.org/abs/1505.01749
github:?https://github.com/gidariss/mrcnn-object-detection
notes:?http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
notes:?http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/

YOLO

You Only Look Once: Unified, Real-Time Object Detection

arxiv:?http://arxiv.org/abs/1506.02640
code:?http://pjreddie.com/darknet/yolo/
github:?https://github.com/pjreddie/darknet
blog:?https://pjreddie.com/publications/yolo/
slides:?https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p
reddit:?https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/
github:?https://github.com/gliese581gg/YOLO_tensorflow
github:?https://github.com/xingwangsfu/caffe-yolo
github:?https://github.com/frankzhangrui/Darknet-Yolo
github:?https://github.com/BriSkyHekun/py-darknet-yolo
github:?https://github.com/tommy-qichang/yolo.torch
github:?https://github.com/frischzenger/yolo-windows
github:?https://github.com/AlexeyAB/yolo-windows
github:?https://github.com/nilboy/tensorflow-yolo

darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++

blog:?https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp
github:?https://github.com/thtrieu/darkflow

Start Training YOLO with Our Own Data

intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
blog:?http://guanghan.info/blog/en/my-works/train-yolo/
github:?https://github.com/Guanghan/darknet

YOLO: Core ML versus MPSNNGraph

intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
blog:?http://machinethink.net/blog/yolo-coreml-versus-mps-graph/
github:?https://github.com/hollance/YOLO-CoreML-MPSNNGraph

TensorFlow YOLO object detection on Android

intro: Real-time object detection on Android using the YOLO network with TensorFlow
github:?https://github.com/natanielruiz/android-yolo

Computer Vision in iOS – Object Detection

blog:?https://sriraghu.com/2017/07/12/computer-vision-in-ios-object-detection/
github:https://github.com/r4ghu/iOS-CoreML-Yolo

YOLOv2

YOLO9000: Better, Faster, Stronger

arxiv:?https://arxiv.org/abs/1612.08242
code:?http://pjreddie.com/yolo9000/
github(Chainer):?https://github.com/leetenki/YOLOv2
github(Keras):?https://github.com/allanzelener/YAD2K
github(PyTorch):?https://github.com/longcw/yolo2-pytorch
github(Tensorflow):?https://github.com/hizhangp/yolo_tensorflow
github(Windows):?https://github.com/AlexeyAB/darknet
github:?https://github.com/choasUp/caffe-yolo9000
github:?https://github.com/philipperemy/yolo-9000

darknet_scripts

intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
github:?https://github.com/Jumabek/darknet_scripts

Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2

github:?https://github.com/AlexeyAB/Yolo_mark

LightNet: Bringing pjreddie’s DarkNet out of the shadows

github:?https://github.com//explosion/lightnet

YOLO v2 Bounding Box Tool

intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
github:?https://github.com/Cartucho/yolo-boundingbox-labeler-GUI

YOLOv3

YOLOv3: An Incremental Improvement

project page:?https://pjreddie.com/darknet/yolo/
arxiv:?https://arxiv.org/abs/1804.02767

AttentionNet: Aggregating Weak Directions for Accurate Object Detection

intro: ICCV 2015
intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection task
arxiv:?http://arxiv.org/abs/1506.07704
slides:?https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf
slides:?http://image-net.org/challenges/talks/lunit-kaist-slide.pdf

DenseBox

DenseBox: Unifying Landmark Localization with End to End Object Detection

arxiv:?http://arxiv.org/abs/1509.04874
demo:?http://pan.baidu.com/s/1mgoWWsS
KITTI result:?http://www.cvlibs.net/datasets/kitti/eval_object.php

SSD

SSD: Single Shot MultiBox Detector

intro: ECCV 2016 Oral
arxiv:?http://arxiv.org/abs/1512.02325
paper:?http://www.cs.unc.edu/~wliu/papers/ssd.pdf
slides:?http://www.cs.unc.edu/~wliu/papers/ssd_eccv2016_slide.pdf
github(Official):?https://github.com/weiliu89/caffe/tree/ssd
video:?http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973
github:?https://github.com/zhreshold/mxnet-ssd
github:?https://github.com/zhreshold/mxnet-ssd.cpp
github:?https://github.com/rykov8/ssd_keras
github:?https://github.com/balancap/SSD-Tensorflow
github:?https://github.com/amdegroot/ssd.pytorch
github(Caffe):?https://github.com/chuanqi305/MobileNet-SSD
What’s the diffience in performance between this new code you pushed and the previous code? #327
https://github.com/weiliu89/caffe/issues/327

DSSD

DSSD : Deconvolutional Single Shot Detector

intro: UNC Chapel Hill & Amazon Inc
arxiv:?https://arxiv.org/abs/1701.06659
github:?https://github.com/chengyangfu/caffe/tree/dssd
github:?https://github.com/MTCloudVision/mxnet-dssd
demo:?http://120.52.72.53/www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd_lalaland.mp4

Enhancement of SSD by concatenating feature maps for object detection

intro: rainbow SSD (R-SSD)
arxiv:?https://arxiv.org/abs/1705.09587

Context-aware Single-Shot Detector

keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
arxiv:?https://arxiv.org/abs/1707.08682

Feature-Fused SSD: Fast Detection for Small Objects

https://arxiv.org/abs/1709.05054

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

https://arxiv.org/abs/1712.00960

Weaving Multi-scale Context for Single Shot Detector

intro: WeaveNet
keywords: fuse multi-scale information
arxiv:?https://arxiv.org/abs/1712.03149

ESSD

Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network

arxiv:?https://arxiv.org/abs/1801.05918

Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection

arxiv:?https://arxiv.org/abs/1802.06488

MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects

intro: Zhengzhou University
arxiv:?https://arxiv.org/abs/1805.07009

Inside-Outside Net (ION)

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

intro: “0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it”.
arxiv:?http://arxiv.org/abs/1512.04143
slides:?http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf
coco-leaderboard:?http://mscoco.org/dataset/#detections-leaderboard

Adaptive Object Detection Using Adjacency and Zoom Prediction

intro: CVPR 2016. AZ-Net
arxiv:?http://arxiv.org/abs/1512.07711
github:?https://github.com/luyongxi/az-net
youtube:?https://www.youtube.com/watch?v=YmFtuNwxaNM

G-CNN: an Iterative Grid Based Object Detector

arxiv:?http://arxiv.org/abs/1512.07729

Factors in Finetuning Deep Model for object detection

Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution

intro: CVPR 2016.rank 3rd for provided data and 2nd for external data on ILSVRC 2015 object detection
project page:?http://www.ee.cuhk.edu.hk/~wlouyang/projects/ImageNetFactors/CVPR16.html
arxiv:?http://arxiv.org/abs/1601.05150

We don’t need no bounding-boxes: Training object class detectors using only human verification

arxiv:?http://arxiv.org/abs/1602.08405

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

arxiv:?http://arxiv.org/abs/1604.00600

A MultiPath Network for Object Detection

intro: BMVC 2016. Facebook AI Research (FAIR)
arxiv:?http://arxiv.org/abs/1604.02135
github:?https://github.com/facebookresearch/multipathnet

CRAFT

CRAFT Objects from Images

intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN
project page:?http://byangderek.github.io/projects/craft.html
arxiv:?https://arxiv.org/abs/1604.03239
paper:?http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_CRAFT_Objects_From_CVPR_2016_paper.pdf
github:?https://github.com/byangderek/CRAFT

OHEM

Training Region-based Object Detectors with Online Hard Example Mining

intro: CVPR 2016 Oral. Online hard example mining (OHEM)
arxiv:?http://arxiv.org/abs/1604.03540
paper:?http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.pdf
github(Official):?https://github.com/abhi2610/ohem
author page:?http://abhinav-shrivastava.info/

S-OHEM: Stratified Online Hard Example Mining for Object Detection

arxiv:?https://arxiv.org/abs/1705.02233

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

intro: CVPR 2016
keywords: scale-dependent pooling (SDP), cascaded rejection classifiers (CRC)
paper:?http://www-personal.umich.edu/~wgchoi/SDP-CRC_camready.pdf

R-FCN

R-FCN: Object Detection via Region-based Fully Convolutional Networks

arxiv:?http://arxiv.org/abs/1605.06409
github:?https://github.com/daijifeng001/R-FCN
github(MXNet):?https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn
github:?https://github.com/Orpine/py-R-FCN
github:?https://github.com/PureDiors/pytorch_RFCN
github:?https://github.com/bharatsingh430/py-R-FCN-multiGPU
github:?https://github.com/xdever/RFCN-tensorflow

R-FCN-3000 at 30fps: Decoupling Detection and Classification

arxiv:?https://arxiv.org/abs/1712.01802

Recycle deep features for better object detection

arxiv:?http://arxiv.org/abs/1607.05066

MS-CNN

A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

intro: ECCV 2016
intro: 640×480: 15 fps, 960×720: 8 fps
arxiv:?http://arxiv.org/abs/1607.07155
github:?https://github.com/zhaoweicai/mscnn
poster:?http://www.eccv2016.org/files/posters/P-2B-38.pdf

Multi-stage Object Detection with Group Recursive Learning

intro: VOC2007: 78.6%, VOC2012: 74.9%
arxiv:?http://arxiv.org/abs/1608.05159

Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

intro: WACV 2017. SubCNN
arxiv:?http://arxiv.org/abs/1604.04693
github:?https://github.com/tanshen/SubCNN

PVANET

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). Continuation of arXiv:1608.08021
arxiv:?https://arxiv.org/abs/1611.08588
github:?https://github.com/sanghoon/pva-faster-rcnn
leaderboard(PVANet 9.0):?http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

GBD-Net

Gated Bi-directional CNN for Object Detection

intro: The Chinese University of Hong Kong & Sensetime Group Limited
paper:?http://link.springer.com/chapter/10.1007/978-3-319-46478-7_22
mirror:?https://pan.baidu.com/s/1dFohO7v

Crafting GBD-Net for Object Detection

intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo
intro: gated bi-directional CNN (GBD-Net)
arxiv:?https://arxiv.org/abs/1610.02579
github:?https://github.com/craftGBD/craftGBD

StuffNet: Using ‘Stuff’ to Improve Object Detection

arxiv:?https://arxiv.org/abs/1610.05861

Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

arxiv:?https://arxiv.org/abs/1610.09609

Hierarchical Object Detection with Deep Reinforcement Learning

intro: Deep Reinforcement Learning Workshop (NIPS 2016)
project page:?https://imatge-upc.github.io/detection-2016-nipsws/
arxiv:?https://arxiv.org/abs/1611.03718
slides:?http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning
github:?https://github.com/imatge-upc/detection-2016-nipsws
blog:?http://jorditorres.org/nips/

Learning to detect and localize many objects from few examples

arxiv:?https://arxiv.org/abs/1611.05664

Speed/accuracy trade-offs for modern convolutional object detectors

intro: CVPR 2017. Google Research
arxiv:?https://arxiv.org/abs/1611.10012

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

arxiv:?https://arxiv.org/abs/1612.01051
github:?https://github.com/BichenWuUCB/squeezeDet
github:?https://github.com/fregu856/2D_detection

Feature Pyramid Network (FPN)

Feature Pyramid Networks for Object Detection

intro: Facebook AI Research
arxiv:?https://arxiv.org/abs/1612.03144

Action-Driven Object Detection with Top-Down Visual Attentions

arxiv:?https://arxiv.org/abs/1612.06704

Beyond Skip Connections: Top-Down Modulation for Object Detection

intro: CMU & UC Berkeley & Google Research
arxiv:?https://arxiv.org/abs/1612.06851

Wide-Residual-Inception Networks for Real-time Object Detection

intro: Inha University
arxiv:?https://arxiv.org/abs/1702.01243

Attentional Network for Visual Object Detection

intro: University of Maryland & Mitsubishi Electric Research Laboratories
arxiv:?https://arxiv.org/abs/1702.01478

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

keykwords: CC-Net
intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
arxiv:?https://arxiv.org/abs/1702.07054

DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling

intro: ICCV 2017 (poster)
arxiv:?https://arxiv.org/abs/1703.10295

Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries

intro: CVPR 2017
arxiv:?https://arxiv.org/abs/1704.03944

Spatial Memory for Context Reasoning in Object Detection

arxiv:?https://arxiv.org/abs/1704.04224

Accurate Single Stage Detector Using Recurrent Rolling Convolution

intro: CVPR 2017. SenseTime
keywords: Recurrent Rolling Convolution (RRC)
arxiv:?https://arxiv.org/abs/1704.05776
github:?https://github.com/xiaohaoChen/rrc_detection

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

arxiv:?https://arxiv.org/abs/1704.05775

LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems

intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc
arxiv:?https://arxiv.org/abs/1705.05922

Point Linking Network for Object Detection

intro: Point Linking Network (PLN)
arxiv:?https://arxiv.org/abs/1706.03646

Perceptual Generative Adversarial Networks for Small Object Detection

arxiv:?https://arxiv.org/abs/1706.05274

Few-shot Object Detection

arxiv:?https://arxiv.org/abs/1706.08249

Yes-Net: An effective Detector Based on Global Information

arxiv:?https://arxiv.org/abs/1706.09180

SMC Faster R-CNN: Toward a scene-specialized multi-object detector

arxiv:?https://arxiv.org/abs/1706.10217

Towards lightweight convolutional neural networks for object detection

arxiv:?https://arxiv.org/abs/1707.01395

RON: Reverse Connection with Objectness Prior Networks for Object Detection

intro: CVPR 2017
arxiv:?https://arxiv.org/abs/1707.01691
github:?https://github.com/taokong/RON

Mimicking Very Efficient Network for Object Detection

intro: CVPR 2017. SenseTime & Beihang University
paper:?http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf

Residual Features and Unified Prediction Network for Single Stage Detection

https://arxiv.org/abs/1707.05031

Deformable Part-based Fully Convolutional Network for Object Detection

intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
arxiv:?https://arxiv.org/abs/1707.06175

Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors

intro: ICCV 2017
arxiv:?https://arxiv.org/abs/1707.06399

Recurrent Scale Approximation for Object Detection in CNN

intro: ICCV 2017
keywords: Recurrent Scale Approximation (RSA)
arxiv:?https://arxiv.org/abs/1707.09531
github:?https://github.com/sciencefans/RSA-for-object-detection

DSOD

DSOD: Learning Deeply Supervised Object Detectors from Scratch

intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
arxiv:?https://arxiv.org/abs/1708.01241
github:?https://github.com/szq0214/DSOD

Object Detection from Scratch with Deep Supervision

arxiv:?https://arxiv.org/abs/1809.09294

##RetinaNet

Focal Loss for Dense Object Detection

intro: ICCV 2017 Best student paper award. Facebook AI Research
keywords: RetinaNet
arxiv:?https://arxiv.org/abs/1708.02002

Focal Loss Dense Detector for Vehicle Surveillance

arxiv:?https://arxiv.org/abs/1803.01114

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

intro: ICCV 2017
arxiv:?https://arxiv.org/abs/1708.02863

Incremental Learning of Object Detectors without Catastrophic Forgetting

intro: ICCV 2017. Inria
arxiv:?https://arxiv.org/abs/1708.06977

Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection

arxiv:?https://arxiv.org/abs/1709.04347

StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection

arxiv:?https://arxiv.org/abs/1709.05788

Dynamic Zoom-in Network for Fast Object Detection in Large Images

https://arxiv.org/abs/1711.05187

Zero-Annotation Object Detection with Web Knowledge Transfer

intro: NTU, Singapore & Amazon
keywords: multi-instance multi-label domain adaption learning framework
arxiv:?https://arxiv.org/abs/1711.05954

MegDet

MegDet: A Large Mini-Batch Object Detector

intro: Peking University & Tsinghua University & Megvii Inc
arxiv:?https://arxiv.org/abs/1711.07240

Single-Shot Refinement Neural Network for Object Detection

arxiv:?https://arxiv.org/abs/1711.06897
github:?https://github.com/sfzhang15/RefineDet
github:?https://github.com/MTCloudVision/RefineDet-Mxnet

Receptive Field Block Net for Accurate and Fast Object Detection

intro: RFBNet
arxiv:?https://arxiv.org/abs/1711.07767
github:?https://github.com//ruinmessi/RFBNet

An Analysis of Scale Invariance in Object Detection - SNIP

intro: CVPR 2018
arxiv:?https://arxiv.org/abs/1711.08189
github:?https://github.com/bharatsingh430/snip

Feature Selective Networks for Object Detection

arxiv:?https://arxiv.org/abs/1711.08879

Learning a Rotation Invariant Detector with Rotatable Bounding Box

arxiv:?https://arxiv.org/abs/1711.09405
github(official, Caffe):?https://github.com/liulei01/DRBox

Scalable Object Detection for Stylized Objects

intro: Microsoft AI & Research Munich
arxiv:?https://arxiv.org/abs/1711.09822

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

arxiv:?https://arxiv.org/abs/1712.00886
github:?https://github.com/szq0214/GRP-DSOD

Deep Regionlets for Object Detection

keywords: region selection network, gating network
arxiv:?https://arxiv.org/abs/1712.02408

Training and Testing Object Detectors with Virtual Images

intro: IEEE/CAA Journal of Automatica Sinica
arxiv:?https://arxiv.org/abs/1712.08470

Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video

keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
arxiv:?https://arxiv.org/abs/1712.08832

Spot the Difference by Object Detection

intro: Tsinghua University & JD Group
arxiv:?https://arxiv.org/abs/1801.01051

Localization-Aware Active Learning for Object Detection

arxiv:?https://arxiv.org/abs/1801.05124

Object Detection with Mask-based Feature Encoding

arxiv:?https://arxiv.org/abs/1802.03934

LSTD: A Low-Shot Transfer Detector for Object Detection

intro: AAAI 2018
arxiv:?https://arxiv.org/abs/1803.01529

Domain Adaptive Faster R-CNN for Object Detection in the Wild

intro: CVPR 2018. ETH Zurich & ESAT/PSI
arxiv:?https://arxiv.org/abs/1803.03243
github(official. Caffe):?https://github.com/yuhuayc/da-faster-rcnn

Pseudo Mask Augmented Object Detection

arxiv:?https://arxiv.org/abs/1803.05858

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

intro: ECCV 2018
keywords: DCR V1
arxiv:?https://arxiv.org/abs/1803.06799
github(official, MXNet):?https://github.com/bowenc0221/Decoupled-Classification-Refinement

Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection

keywords: DCR V2
arxiv:?https://arxiv.org/abs/1810.04002
github(official, MXNet):?https://github.com/bowenc0221/Decoupled-Classification-Refinement

Learning Region Features for Object Detection

intro: Peking University & MSRA
arxiv:?https://arxiv.org/abs/1803.07066

Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection

intro: Singapore Management University & Zhejiang University
arxiv:?https://arxiv.org/abs/1803.08208

Object Detection for Comics using Manga109 Annotations

intro: University of Tokyo & National Institute of Informatics, Japan
arxiv:?https://arxiv.org/abs/1803.08670

Task-Driven Super Resolution: Object Detection in Low-resolution Images

arxiv:?https://arxiv.org/abs/1803.11316

Transferring Common-Sense Knowledge for Object Detection

arxiv:?https://arxiv.org/abs/1804.01077

Multi-scale Location-aware Kernel Representation for Object Detection

intro: CVPR 2018
arxiv:?https://arxiv.org/abs/1804.00428
github:?https://github.com/Hwang64/MLKP

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

intro: National University of Defense Technology
arxiv:?https://arxiv.org/abs/1804.04606

DetNet: A Backbone network for Object Detection

intro: Tsinghua University & Megvii Inc
arxiv:?https://arxiv.org/abs/1804.06215

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

arxiv:?https://arxiv.org/abs/1804.05810

AdvDetPatch: Attacking Object Detectors with Adversarial Patches

arxiv:?https://arxiv.org/abs/1806.02299

Attacking Object Detectors via Imperceptible Patches on Background

https://arxiv.org/abs/1809.05966

Physical Adversarial Examples for Object Detectors

intro: WOOT 2018
arxiv:?https://arxiv.org/abs/1807.07769

Quantization Mimic: Towards Very Tiny CNN for Object Detection

arxiv:?https://arxiv.org/abs/1805.02152

Object detection at 200 Frames Per Second

intro: United Technologies Research Center-Ireland
arxiv:?https://arxiv.org/abs/1805.06361

Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images

intro: CVPR 2018 Deep Vision Workshop
arxiv:?https://arxiv.org/abs/1805.11778

SNIPER: Efficient Multi-Scale Training

arxiv:?https://arxiv.org/abs/1805.09300
github:?https://github.com/mahyarnajibi/SNIPER

Soft Sampling for Robust Object Detection

arxiv:?https://arxiv.org/abs/1806.06986

MetaAnchor: Learning to Detect Objects with Customized Anchors

intro: Megvii Inc (Face++) & Fudan University
arxiv:?https://arxiv.org/abs/1807.00980

Localization Recall Precision (LRP): A New Performance Metric for Object Detection

intro: ECCV 2018. Middle East Technical University
arxiv:?https://arxiv.org/abs/1807.01696
github:?https://github.com/cancam/LRP

Auto-Context R-CNN

intro: Rejected by ECCV18
arxiv:?https://arxiv.org/abs/1807.02842

Pooling Pyramid Network for Object Detection

intro: Google AI Perception
arxiv:?https://arxiv.org/abs/1807.03284

Modeling Visual Context is Key to Augmenting Object Detection Datasets

intro: ECCV 2018
arxiv:?https://arxiv.org/abs/1807.07428

Dual Refinement Network for Single-Shot Object Detection

arxiv:?https://arxiv.org/abs/1807.08638

Acquisition of Localization Confidence for Accurate Object Detection

intro: ECCV 2018
arxiv:?https://arxiv.org/abs/1807.11590
gihtub:?https://github.com/vacancy/PreciseRoIPooling

CornerNet: Detecting Objects as Paired Keypoints

intro: ECCV 2018
keywords: IoU-Net, PreciseRoIPooling
arxiv:?https://arxiv.org/abs/1808.01244
github:?https://github.com/umich-vl/CornerNet

Unsupervised Hard Example Mining from Videos for Improved Object Detection

intro: ECCV 2018
arxiv:?https://arxiv.org/abs/1808.04285

SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection

arxiv:?https://arxiv.org/abs/1808.04974

A Survey of Modern Object Detection Literature using Deep Learning

arxiv:?https://arxiv.org/abs/1808.07256

Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages

intro: BMVC 2018
arxiv:?https://arxiv.org/abs/1807.11013
github:?https://github.com/lyxok1/Tiny-DSOD

Deep Feature Pyramid Reconfiguration for Object Detection

intro: ECCV 2018
arxiv:?https://arxiv.org/abs/1808.07993

MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection

intro: ICPR 2018
arxiv:?https://arxiv.org/abs/1809.01791

Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

https://arxiv.org/abs/1809.03193

Deep Learning for Generic Object Detection: A Survey

https://arxiv.org/abs/1809.02165

Non-Maximum Suppression (NMS)

End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression

intro: CVPR 2015
arxiv:?http://arxiv.org/abs/1411.5309
paper:?http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wan_End-to-End_Integration_of_2015_CVPR_paper.pdf

A convnet for non-maximum suppression

arxiv:?http://arxiv.org/abs/1511.06437
Improving Object Detection With One Line of Code

Soft-NMS – Improving Object Detection With One Line of Code

intro: ICCV 2017. University of Maryland
keywords: Soft-NMS
arxiv:?https://arxiv.org/abs/1704.04503
github:?https://github.com/bharatsingh430/soft-nms

Learning non-maximum suppression

intro: CVPR 2017
project page:?https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/learning-nms/
arxiv:?https://arxiv.org/abs/1705.02950
github:?https://github.com/hosang/gossipnet

Relation Networks for Object Detection

intro: CVPR 2018 oral
arxiv:?https://arxiv.org/abs/1711.11575
github(official, MXNet):?https://github.com/msracver/Relation-Networks-for-Object-Detection

Adversarial Examples

Adversarial Examples that Fool Detectors

intro: University of Illinois
arxiv:?https://arxiv.org/abs/1712.02494

Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

project page:?http://nicholas.carlini.com/code/nn_breaking_detection/
arxiv:?https://arxiv.org/abs/1705.07263
github:?https://github.com/carlini/nn_breaking_detection

Weakly Supervised Object Detection

Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection

intro: CVPR 2016
arxiv:?http://arxiv.org/abs/1604.05766

Weakly supervised object detection using pseudo-strong labels

arxiv:?http://arxiv.org/abs/1607.04731

Saliency Guided End-to-End Learning for Weakly Supervised Object Detection

intro: IJCAI 2017
arxiv:?https://arxiv.org/abs/1706.06768

Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

intro: TPAMI 2017. National Institutes of Health (NIH) Clinical Center
arxiv:?https://arxiv.org/abs/1801.03145

Video Object Detection

Learning Object Class Detectors from Weakly Annotated Video

intro: CVPR 2012
paper:?https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_00905.pdf

Analysing domain shift factors between videos and images for object detection

arxiv:?https://arxiv.org/abs/1501.01186

Video Object Recognition

slides:?http://vision.princeton.edu/courses/COS598/2015sp/slides/VideoRecog/Video Object Recognition.pptx

Deep Learning for Saliency Prediction in Natural Video

intro: Submitted on 12 Jan 2016
keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
paper:?https://hal.archives-ouvertes.fr/hal-01251614/document

T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
arxiv:?http://arxiv.org/abs/1604.02532
github:?https://github.com/myfavouritekk/T-CNN

Object Detection from Video Tubelets with Convolutional Neural Networks

intro: CVPR 2016 Spotlight paper
arxiv:?https://arxiv.org/abs/1604.04053
paper:?http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf
gihtub:?https://github.com/myfavouritekk/vdetlib

Object Detection in Videos with Tubelets and Multi-context Cues

intro: SenseTime Group
slides:?http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf
slides:?http://image-net.org/challenges/talks/Object Detection in Videos with Tubelets and Multi-context Cues - Final.pdf

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks

intro: BMVC 2016
keywords: pseudo-labeler
arxiv:?http://arxiv.org/abs/1607.04648
paper:?http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf

CNN Based Object Detection in Large Video Images

intro: WangTao @ 愛奇藝
keywords: object retrieval, object detection, scene classification
slides:?http://on-demand.gputechconf.com/gtc/2016/presentation/s6362-wang-tao-cnn-based-object-detection-large-video-images.pdf

Object Detection in Videos with Tubelet Proposal Networks

arxiv:?https://arxiv.org/abs/1702.06355

Flow-Guided Feature Aggregation for Video Object Detection

intro: MSRA
arxiv:?https://arxiv.org/abs/1703.10025

Video Object Detection using Faster R-CNN

blog:?http://andrewliao11.github.io/object_detection/faster_rcnn/
github:?https://github.com/andrewliao11/py-faster-rcnn-imagenet

Improving Context Modeling for Video Object Detection and Tracking

http://image-net.org/challenges/talks_2017/ilsvrc2017_short(poster).pdf

Temporal Dynamic Graph LSTM for Action-driven Video Object Detection

intro: ICCV 2017
arxiv:?https://arxiv.org/abs/1708.00666

Mobile Video Object Detection with Temporally-Aware Feature Maps

arxiv:?https://arxiv.org/abs/1711.06368

Towards High Performance Video Object Detection

arxiv:?https://arxiv.org/abs/1711.11577

Impression Network for Video Object Detection

arxiv:?https://arxiv.org/abs/1712.05896

Spatial-Temporal Memory Networks for Video Object Detection

arxiv:?https://arxiv.org/abs/1712.06317

3D-DETNet: a Single Stage Video-Based Vehicle Detector

arxiv:?https://arxiv.org/abs/1801.01769

Object Detection in Videos by Short and Long Range Object Linking

arxiv:?https://arxiv.org/abs/1801.09823

Object Detection in Video with Spatiotemporal Sampling Networks

intro: University of Pennsylvania, 2Dartmouth College
arxiv:?https://arxiv.org/abs/1803.05549

Towards High Performance Video Object Detection for Mobiles

intro: Microsoft Research Asia
arxiv:?https://arxiv.org/abs/1804.05830

Optimizing Video Object Detection via a Scale-Time Lattice

intro: CVPR 2018
project page:?http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice/
arxiv:?https://arxiv.org/abs/1804.05472
github:?https://github.com/hellock/scale-time-lattice

Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing

https://arxiv.org/abs/1809.01701

Object Detection on Mobile Devices

Pelee: A Real-Time Object Detection System on Mobile Devices

intro: ICLR 2018 workshop track
intro: based on the SSD
arxiv:?https://arxiv.org/abs/1804.06882
github:?https://github.com/Robert-JunWang/Pelee

Object Detection in 3D

Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

arxiv:?https://arxiv.org/abs/1609.06666

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

intro: Valeo Schalter und Sensoren GmbH & Ilmenau University of Technology
arxiv:?https://arxiv.org/abs/1803.06199

Focal Loss in 3D Object Detection

arxiv:?https://arxiv.org/abs/1809.06065
github:?https://github.com/pyun-ram/FL3D

Object Detection on RGB-D

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

arxiv:?http://arxiv.org/abs/1407.5736

Differential Geometry Boosts Convolutional Neural Networks for Object Detection

intro: CVPR 2016
paper:?http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w23/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation

arxiv:?https://arxiv.org/abs/1703.03347

Zero-Shot Object Detection

Zero-Shot Detection

intro: Australian National University
keywords: YOLO
arxiv:?https://arxiv.org/abs/1803.07113

Zero-Shot Object Detection

arxiv:?https://arxiv.org/abs/1804.04340

Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

intro: Australian National University
arxiv:?https://arxiv.org/abs/1803.06049

Zero-Shot Object Detection by Hybrid Region Embedding

intro: Middle East Technical University & Hacettepe University
arxiv:?https://arxiv.org/abs/1805.06157

Salient Object Detection

This task involves predicting the salient regions of an image given by human eye fixations.

Best Deep Saliency Detection Models (CVPR 2016 & 2015)

page:?http://i.cs.hku.hk/~yzyu/vision.html

Large-scale optimization of hierarchical features for saliency prediction in natural images

paper:?http://coxlab.org/pdfs/cvpr2014_vig_saliency.pdf

Predicting Eye Fixations using Convolutional Neural Networks

paper:?http://www.escience.cn/system/file?fileId=72648

Saliency Detection by Multi-Context Deep Learning

paper:?http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhao_Saliency_Detection_by_2015_CVPR_paper.pdf

DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

arxiv:?http://arxiv.org/abs/1510.05484

SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

paper:?www.shengfenghe.com/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html

Shallow and Deep Convolutional Networks for Saliency Prediction

intro: CVPR 2016
arxiv:?http://arxiv.org/abs/1603.00845
github:?https://github.com/imatge-upc/saliency-2016-cvpr

Recurrent Attentional Networks for Saliency Detection

intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)
arxiv:?http://arxiv.org/abs/1604.03227

Two-Stream Convolutional Networks for Dynamic Saliency Prediction

arxiv:?http://arxiv.org/abs/1607.04730

Unconstrained Salient Object Detection

Unconstrained Salient Object Detection via Proposal Subset Optimization

intro: CVPR 2016
project page:?http://cs-people.bu.edu/jmzhang/sod.html
paper:?http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD_camera_ready.pdf
github:?https://github.com/jimmie33/SOD
caffe model zoo:?https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-object-proposal-models-for-salient-object-detection

DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

paper:?http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf

Salient Object Subitizing

intro: CVPR 2015
intro: predicting the existence and the number of salient objects in an image using holistic cues
project page:?http://cs-people.bu.edu/jmzhang/sos.html
arxiv:?http://arxiv.org/abs/1607.07525
paper:?http://cs-people.bu.edu/jmzhang/SOS/SOS_preprint.pdf
caffe model zoo:?https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-models-for-salient-object-subitizing

Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
arxiv:?http://arxiv.org/abs/1608.05177

Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs

intro: ECCV 2016
arxiv:?http://arxiv.org/abs/1608.05186

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

arxiv:?http://arxiv.org/abs/1608.08029

A Deep Multi-Level Network for Saliency Prediction

arxiv:?http://arxiv.org/abs/1609.01064

Visual Saliency Detection Based on Multiscale Deep CNN Features

intro: IEEE Transactions on Image Processing
arxiv:?http://arxiv.org/abs/1609.02077

A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection

intro: DSCLRCN
arxiv:?https://arxiv.org/abs/1610.01708

Deeply supervised salient object detection with short connections

intro: IEEE TPAMI 2018 (IEEE CVPR 2017)
arxiv:?https://arxiv.org/abs/1611.04849
github(official, Caffe):?https://github.com/Andrew-Qibin/DSS
github(Tensorflow):?https://github.com/Joker316701882/Salient-Object-Detection

Weakly Supervised Top-down Salient Object Detection

intro: Nanyang Technological University
arxiv:?https://arxiv.org/abs/1611.05345

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

project page:?https://imatge-upc.github.io/saliency-salgan-2017/
arxiv:?https://arxiv.org/abs/1701.01081

Visual Saliency Prediction Using a Mixture of Deep Neural Networks

arxiv:?https://arxiv.org/abs/1702.00372

A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network

arxiv:?https://arxiv.org/abs/1702.00615

Saliency Detection by Forward and Backward Cues in Deep-CNNs

arxiv:?https://arxiv.org/abs/1703.00152

Supervised Adversarial Networks for Image Saliency Detection

arxiv:?https://arxiv.org/abs/1704.07242

Group-wise Deep Co-saliency Detection

arxiv:?https://arxiv.org/abs/1707.07381

Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection

intro: University of Maryland College Park & eBay Inc
arxiv:?https://arxiv.org/abs/1708.00079

Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection

intro: ICCV 2017
arixv:?https://arxiv.org/abs/1708.02001

Learning Uncertain Convolutional Features for Accurate Saliency Detection

intro: Accepted as a poster in ICCV 2017
arxiv:?https://arxiv.org/abs/1708.02031

Deep Edge-Aware Saliency Detection

arxiv:?https://arxiv.org/abs/1708.04366

Self-explanatory Deep Salient Object Detection

intro: National University of Defense Technology, China & National University of Singapore
arxiv:?https://arxiv.org/abs/1708.05595

PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection

arxiv:?https://arxiv.org/abs/1708.06433

DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets

arxiv:?https://arxiv.org/abs/1709.02495

Recurrently Aggregating Deep Features for Salient Object Detection

intro: AAAI 2018
paper:?https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16775/16281

Deep saliency: What is learnt by a deep network about saliency?

intro: 2nd Workshop on Visualisation for Deep Learning in the 34th International Conference On Machine Learning
arxiv:?https://arxiv.org/abs/1801.04261

Contrast-Oriented Deep Neural Networks for Salient Object Detection

intro: TNNLS
arxiv:?https://arxiv.org/abs/1803.11395

Salient Object Detection by Lossless Feature Reflection

intro: IJCAI 2018
arxiv:?https://arxiv.org/abs/1802.06527

HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection

arxiv:?https://arxiv.org/abs/1804.05142

Video Saliency Detection

Deep Learning For Video Saliency Detection

arxiv:?https://arxiv.org/abs/1702.00871

Video Salient Object Detection Using Spatiotemporal Deep Features

arxiv:?https://arxiv.org/abs/1708.01447

Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

arxiv:?https://arxiv.org/abs/1709.06316

Visual Relationship Detection

Visual Relationship Detection with Language Priors

intro: ECCV 2016 oral
paper:?https://cs.stanford.edu/people/ranjaykrishna/vrd/vrd.pdf
github:?https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection

ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection

intro: Visual Phrase reasoning Convolutional Neural Network (ViP-CNN), Visual Phrase Reasoning Structure (VPRS)
arxiv:?https://arxiv.org/abs/1702.07191

Visual Translation Embedding Network for Visual Relation Detection

arxiv:?https://www.arxiv.org/abs/1702.08319

Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection

intro: CVPR 2017 spotlight paper
arxiv:?https://arxiv.org/abs/1703.03054

Detecting Visual Relationships with Deep Relational Networks

intro: CVPR 2017 oral. The Chinese University of Hong Kong
arxiv:?https://arxiv.org/abs/1704.03114

Identifying Spatial Relations in Images using Convolutional Neural Networks

arxiv:?https://arxiv.org/abs/1706.04215

PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN

intro: ICCV
arxiv:?https://arxiv.org/abs/1708.01956

Natural Language Guided Visual Relationship Detection

arxiv:?https://arxiv.org/abs/1711.06032

Detecting Visual Relationships Using Box Attention

intro: Google AI & IST Austria
arxiv:?https://arxiv.org/abs/1807.02136

Google AI Open Images - Visual Relationship Track

intro: Detect pairs of objects in particular relationships
kaggle:?https://www.kaggle.com/c/google-ai-open-images-visual-relationship-track

Context-Dependent Diffusion Network for Visual Relationship Detection

intro: 2018 ACM Multimedia Conference
arxiv:?https://arxiv.org/abs/1809.06213

A Problem Reduction Approach for Visual Relationships Detection

intro: ECCV 2018 Workshop
arxiv:?https://arxiv.org/abs/1809.09828

Face Deteciton

Multi-view Face Detection Using Deep Convolutional Neural Networks

intro: Yahoo
arxiv:?http://arxiv.org/abs/1502.02766
github:?https://github.com/guoyilin/FaceDetection_CNN

From Facial Parts Responses to Face Detection: A Deep Learning Approach

intro: ICCV 2015. CUHK
project page:?http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html
arxiv:?https://arxiv.org/abs/1509.06451
paper:?http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yang_From_Facial_Parts_ICCV_2015_paper.pdf

Compact Convolutional Neural Network Cascade for Face Detection

arxiv:?http://arxiv.org/abs/1508.01292
github:?https://github.com/Bkmz21/FD-Evaluation
github:?https://github.com/Bkmz21/CompactCNNCascade

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

intro: ECCV 2016
arxiv:?https://arxiv.org/abs/1606.00850
github(MXNet):?https://github.com/tfwu/FaceDetection-ConvNet-3D

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

intro: CMU
arxiv:?https://arxiv.org/abs/1606.05413

Towards a Deep Learning Framework for Unconstrained Face Detection

intro: overlap with CMS-RCNN
arxiv:?https://arxiv.org/abs/1612.05322

Supervised Transformer Network for Efficient Face Detection

arxiv:?http://arxiv.org/abs/1607.05477

UnitBox: An Advanced Object Detection Network

intro: ACM MM 2016
keywords: IOULoss
arxiv:?http://arxiv.org/abs/1608.01471

Bootstrapping Face Detection with Hard Negative Examples

author: 萬韶華 @ 小米.
intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset
arxiv:?http://arxiv.org/abs/1608.02236

Grid Loss: Detecting Occluded Faces

intro: ECCV 2016
arxiv:?https://arxiv.org/abs/1609.00129
paper:?http://lrs.icg.tugraz.at/pubs/opitz_eccv_16.pdf
poster:?http://www.eccv2016.org/files/posters/P-2A-34.pdf

A Multi-Scale Cascade Fully Convolutional Network Face Detector

intro: ICPR 2016
arxiv:?http://arxiv.org/abs/1609.03536

MTCNN

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

project page:?https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html
arxiv:?https://arxiv.org/abs/1604.02878
github(official, Matlab):?https://github.com/kpzhang93/MTCNN_face_detection_alignment
github:?https://github.com/pangyupo/mxnet_mtcnn_face_detection
github:?https://github.com/DaFuCoding/MTCNN_Caffe
github(MXNet):?https://github.com/Seanlinx/mtcnn
github:?https://github.com/Pi-DeepLearning/RaspberryPi-FaceDetection-MTCNN-Caffe-With-Motion
github(Caffe):?https://github.com/foreverYoungGitHub/MTCNN
github:?https://github.com/CongWeilin/mtcnn-caffe
github(OpenCV+OpenBlas):?https://github.com/AlphaQi/MTCNN-light
github(Tensorflow+golang):?https://github.com/jdeng/goface

Face Detection using Deep Learning: An Improved Faster RCNN Approach

intro: DeepIR Inc
arxiv:?https://arxiv.org/abs/1701.08289

Faceness-Net: Face Detection through Deep Facial Part Responses

intro: An extended version of ICCV 2015 paper
arxiv:?https://arxiv.org/abs/1701.08393

Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”

intro: CVPR 2017. MP-RCNN, MP-RPN
arxiv:?https://arxiv.org/abs/1703.09145

End-To-End Face Detection and Recognition

arxiv:?https://arxiv.org/abs/1703.10818

Face R-CNN

arxiv:?https://arxiv.org/abs/1706.01061

Face Detection through Scale-Friendly Deep Convolutional Networks

arxiv:?https://arxiv.org/abs/1706.02863

Scale-Aware Face Detection

intro: CVPR 2017. SenseTime & Tsinghua University
arxiv:?https://arxiv.org/abs/1706.09876

Detecting Faces Using Inside Cascaded Contextual CNN

intro: CVPR 2017. Tencent AI Lab & SenseTime
paper:?http://ai.tencent.com/ailab/media/publications/Detecting_Faces_Using_Inside_Cascaded_Contextual_CNN.pdf

Multi-Branch Fully Convolutional Network for Face Detection

arxiv:?https://arxiv.org/abs/1707.06330

SSH: Single Stage Headless Face Detector

intro: ICCV 2017. University of Maryland
arxiv:?https://arxiv.org/abs/1708.03979
github(official, Caffe):?https://github.com/mahyarnajibi/SSH

Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container

arxiv:?https://arxiv.org/abs/1708.04370

FaceBoxes: A CPU Real-time Face Detector with High Accuracy

intro: IJCB 2017
keywords: Rapidly Digested Convolutional Layers (RDCL), Multiple Scale Convolutional Layers (MSCL)
intro: the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images
arxiv:?https://arxiv.org/abs/1708.05234
github(Caffe):?https://github.com/zeusees/FaceBoxes

S3FD: Single Shot Scale-invariant Face Detector

intro: ICCV 2017. Chinese Academy of Sciences
intro: can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images
arxiv:?https://arxiv.org/abs/1708.05237
github(Caffe, official):?https://github.com/sfzhang15/SFD
github:?https://github.com//clcarwin/SFD_pytorch

Detecting Faces Using Region-based Fully Convolutional Networks

arxiv:?https://arxiv.org/abs/1709.05256

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

arxiv:?https://arxiv.org/abs/1709.07326

Face Attention Network: An effective Face Detector for the Occluded Faces

arxiv:?https://arxiv.org/abs/1711.07246

Feature Agglomeration Networks for Single Stage Face Detection

arxiv:?https://arxiv.org/abs/1712.00721

Face Detection Using Improved Faster RCNN

intro: Huawei Cloud BU
arxiv:?https://arxiv.org/abs/1802.02142

PyramidBox: A Context-assisted Single Shot Face Detector

intro: Baidu, Inc
arxiv:?https://arxiv.org/abs/1803.07737

A Fast Face Detection Method via Convolutional Neural Network

intro: Neurocomputing
arxiv:?https://arxiv.org/abs/1803.10103

Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy

intro: CVPR 2018. Beihang University & CUHK & Sensetime
arxiv:?https://arxiv.org/abs/1804.05197

Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks

intro: CVPR 2018
arxiv:?https://arxiv.org/abs/1804.06039
github:?https://github.com/Jack-CV/PCN

SFace: An Efficient Network for Face Detection in Large Scale Variations

intro: Beihang University & Megvii Inc. (Face++)
arxiv:?https://arxiv.org/abs/1804.06559

Survey of Face Detection on Low-quality Images

arxiv:?https://arxiv.org/abs/1804.07362

Anchor Cascade for Efficient Face Detection

intro: The University of Sydney
arxiv:?https://arxiv.org/abs/1805.03363

Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization

intro: IEEE MMSP
arxiv:?https://arxiv.org/abs/1805.12302

Selective Refinement Network for High Performance Face Detection

https://arxiv.org/abs/1809.02693

Detect Small Faces

Finding Tiny Faces

intro: CVPR 2017. CMU
project page:?http://www.cs.cmu.edu/~peiyunh/tiny/index.html
arxiv:?https://arxiv.org/abs/1612.04402
github(official, Matlab):?https://github.com/peiyunh/tiny
github(inference-only):?https://github.com/chinakook/hr101_mxnet
github:?https://github.com/cydonia999/Tiny_Faces_in_Tensorflow

Detecting and counting tiny faces

intro: ENS Paris-Saclay. ExtendedTinyFaces
intro: Detecting and counting small objects - Analysis, review and application to counting
arxiv:?https://arxiv.org/abs/1801.06504
github:?https://github.com/alexattia/ExtendedTinyFaces

Seeing Small Faces from Robust Anchor’s Perspective

intro: CVPR 2018
arxiv:?https://arxiv.org/abs/1802.09058

Face-MagNet: Magnifying Feature Maps to Detect Small Faces

intro: WACV 2018
keywords: Face Magnifier Network (Face-MageNet)
arxiv:?https://arxiv.org/abs/1803.05258
github:?https://github.com/po0ya/face-magnet

Person Head Detection

Context-aware CNNs for person head detection

intro: ICCV 2015
project page:?http://www.di.ens.fr/willow/research/headdetection/
arxiv:?http://arxiv.org/abs/1511.07917
github:?https://github.com/aosokin/cnn_head_detection

Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture

arxiv:?https://arxiv.org/abs/1803.09256

A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications

https://arxiv.org/abs/1809.03336

FCHD: A fast and accurate head detector

arxiv:?https://arxiv.org/abs/1809.08766
github(PyTorch, official):?https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector

Pedestrian Detection / People Detection

Pedestrian Detection aided by Deep Learning Semantic Tasks

intro: CVPR 2015
project page:?http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/
arxiv:?http://arxiv.org/abs/1412.0069

Deep Learning Strong Parts for Pedestrian Detection

intro: ICCV 2015. CUHK. DeepParts
intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset
paper:?http://personal.ie.cuhk.edu.hk/~pluo/pdf/tianLWTiccv15.pdf

Taking a Deeper Look at Pedestrians

intro: CVPR 2015
arxiv:?https://arxiv.org/abs/1501.05790

Convolutional Channel Features

intro: ICCV 2015
arxiv:?https://arxiv.org/abs/1504.07339
github:?https://github.com/byangderek/CCF

End-to-end people detection in crowded scenes

arxiv:?http://arxiv.org/abs/1506.04878
github:?https://github.com/Russell91/reinspect
ipn:?http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation_reinspect.ipynb
youtube:?https://www.youtube.com/watch?v=QeWl0h3kQ24

Learning Complexity-Aware Cascades for Deep Pedestrian Detection

intro: ICCV 2015
arxiv:?https://arxiv.org/abs/1507.05348

Deep convolutional neural networks for pedestrian detection

arxiv:?http://arxiv.org/abs/1510.03608
github:?https://github.com/DenisTome/DeepPed

Scale-aware Fast R-CNN for Pedestrian Detection

arxiv:?https://arxiv.org/abs/1510.08160

New algorithm improves speed and accuracy of pedestrian detection

blog:?http://www.eurekalert.org/pub_releases/2016-02/uoc–nai020516.php

Pushing the Limits of Deep CNNs for Pedestrian Detection

intro: “set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from 11.7% to 8.9%”
arxiv:?http://arxiv.org/abs/1603.04525

A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

arxiv:?http://arxiv.org/abs/1607.04436

A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation

arxiv:?http://arxiv.org/abs/1607.04441

Is Faster R-CNN Doing Well for Pedestrian Detection?

intro: ECCV 2016
arxiv:?http://arxiv.org/abs/1607.07032
github:?https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian

Unsupervised Deep Domain Adaptation for Pedestrian Detection

intro: ECCV Workshop 2016
arxiv:?https://arxiv.org/abs/1802.03269

Reduced Memory Region Based Deep Convolutional Neural Network Detection

intro: IEEE 2016 ICCE-Berlin
arxiv:?http://arxiv.org/abs/1609.02500

Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection

arxiv:?https://arxiv.org/abs/1610.03466

Detecting People in Artwork with CNNs

intro: ECCV 2016 Workshops
arxiv:?https://arxiv.org/abs/1610.08871

Multispectral Deep Neural Networks for Pedestrian Detection

intro: BMVC 2016 oral
arxiv:?https://arxiv.org/abs/1611.02644

Deep Multi-camera People Detection

arxiv:?https://arxiv.org/abs/1702.04593

Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters

intro: CVPR 2017
project page:?http://ml.cs.tsinghua.edu.cn:5000/publications/synunity/
arxiv:?https://arxiv.org/abs/1703.06283
github(Tensorflow):?https://github.com/huangshiyu13/RPNplus

What Can Help Pedestrian Detection?

intro: CVPR 2017. Tsinghua University & Peking University & Megvii Inc.
keywords: Faster R-CNN, HyperLearner
arxiv:?https://arxiv.org/abs/1705.02757
paper:?http://openaccess.thecvf.com/content_cvpr_2017/papers/Mao_What_Can_Help_CVPR_2017_paper.pdf

Illuminating Pedestrians via Simultaneous Detection & Segmentation

arxiv:?https://arxiv.org/abs/1706.08564

Rotational Rectification Network for Robust Pedestrian Detection

intro: CMU & Volvo Construction
arxiv:?https://arxiv.org/abs/1706.08917

STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos

intro: The University of North Carolina at Chapel Hill
arxiv:?https://arxiv.org/abs/1707.09100

Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy

arxiv:?https://arxiv.org/abs/1709.00235

Repulsion Loss: Detecting Pedestrians in a Crowd

arxiv:?https://arxiv.org/abs/1711.07752

Aggregated Channels Network for Real-Time Pedestrian Detection

arxiv:?https://arxiv.org/abs/1801.00476

Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection

intro: State Key Lab of CAD&CG, Zhejiang University
arxiv:?https://arxiv.org/abs/1803.05347

Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection

arxiv:?https://arxiv.org/abs/1804.00872

Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

arxiv:?https://arxiv.org/abs/1804.02047

PCN: Part and Context Information for Pedestrian Detection with CNNs

intro: British Machine Vision Conference(BMVC) 2017
arxiv:?https://arxiv.org/abs/1804.04483

Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation

intro: ECCV 2018. Hikvision Research Institute
arxiv:?https://arxiv.org/abs/1807.01438

Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

intro: ECCV 2018
arxiv:?https://arxiv.org/abs/1807.08407

Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation

intro: BMVC 2018
arxiv:?https://arxiv.org/abs/1808.04818

Vehicle Detection

DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

intro: ECCV 2016
arxiv:?http://arxiv.org/abs/1607.04564

Evolving Boxes for fast Vehicle Detection

arxiv:?https://arxiv.org/abs/1702.00254

Fine-Grained Car Detection for Visual Census Estimation

intro: AAAI 2016
arxiv:?https://arxiv.org/abs/1709.02480

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

intro: IEEE Transactions on Intelligent Transportation Systems (T-ITS)
arxiv:?https://arxiv.org/abs/1804.00433

Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data

intro: UC Berkeley
arxiv:?https://arxiv.org/abs/1808.08603

Traffic-Sign Detection

Traffic-Sign Detection and Classification in the Wild

intro: CVPR 2016
project page(code+dataset):?http://cg.cs.tsinghua.edu.cn/traffic-sign/
paper:?http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf
code & model:?http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/newdata0411.zip

Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data

intro: CVPR 2017 workshop
paper:?http://openaccess.thecvf.com/content_cvpr_2017_workshops/w9/papers/Jensen_Evaluating_State-Of-The-Art_Object_CVPR_2017_paper.pdf

Detecting Small Signs from Large Images

intro: IEEE Conference on Information Reuse and Integration (IRI) 2017 oral
arxiv:?https://arxiv.org/abs/1706.08574

Localized Traffic Sign Detection with Multi-scale Deconvolution Networks

arxiv:?https://arxiv.org/abs/1804.10428

Detecting Traffic Lights by Single Shot Detection

intro: ITSC 2018
arxiv:?https://arxiv.org/abs/1805.02523

A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection

intro: IEEE 15th Conference on Computer and Robot Vision
arxiv:?https://arxiv.org/abs/1806.07987
demo:?https://www.youtube.com/watch?v=_YmogPzBXOw&feature=youtu.be

Skeleton Detection

Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

arxiv:?http://arxiv.org/abs/1603.09446
github:?https://github.com/zeakey/DeepSkeleton

DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images

arxiv:?http://arxiv.org/abs/1609.03659

SRN: Side-output Residual Network for Object Symmetry Detection in the Wild

intro: CVPR 2017
arxiv:?https://arxiv.org/abs/1703.02243
github:?https://github.com/KevinKecc/SRN

Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

arxiv:?https://arxiv.org/abs/1801.01849

Fruit Detection

Deep Fruit Detection in Orchards

arxiv:?https://arxiv.org/abs/1610.03677

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

intro: The Journal of Field Robotics in May 2016
project page:?http://confluence.acfr.usyd.edu.au/display/AGPub/
arxiv:?https://arxiv.org/abs/1610.08120

Shadow Detection

Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network

arxiv:?https://arxiv.org/abs/1709.09283

A+D-Net: Shadow Detection with Adversarial Shadow Attenuation

arxiv:?https://arxiv.org/abs/1712.01361

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

arxiv:?https://arxiv.org/abs/1712.02478

Direction-aware Spatial Context Features for Shadow Detection

intro: CVPR 2018
arxiv:?https://arxiv.org/abs/1712.04142

Direction-aware Spatial Context Features for Shadow Detection and Removal

intro: The Chinese University of Hong Kong & The Hong Kong Polytechnic University
arxiv:?https://arxiv.org/abs/1805.04635

Others Detection

Deep Deformation Network for Object Landmark Localization

arxiv:?http://arxiv.org/abs/1605.01014

Fashion Landmark Detection in the Wild

intro: ECCV 2016
project page:?http://personal.ie.cuhk.edu.hk/~lz013/projects/FashionLandmarks.html
arxiv:?http://arxiv.org/abs/1608.03049
github(Caffe):?https://github.com/liuziwei7/fashion-landmarks

Deep Learning for Fast and Accurate Fashion Item Detection

intro: Kuznech Inc.
intro: MultiBox and Fast R-CNN
paper:?https://kddfashion2016.mybluemix.net/kddfashion_finalSubmissions/Deep Learning for Fast and Accurate Fashion Item Detection.pdf

OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)

github:?https://github.com/geometalab/OSMDeepOD

Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

intro: IEEE SITIS 2016
arxiv:?https://arxiv.org/abs/1611.04357

Associative Embedding:End-to-End Learning for Joint Detection and Grouping

arxiv:?https://arxiv.org/abs/1611.05424

Deep Cuboid Detection: Beyond 2D Bounding Boxes

intro: CMU & Magic Leap
arxiv:?https://arxiv.org/abs/1611.10010

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

arxiv:?https://arxiv.org/abs/1612.03019

Deep Learning Logo Detection with Data Expansion by Synthesising Context

arxiv:?https://arxiv.org/abs/1612.09322

Scalable Deep Learning Logo Detection

arxiv:?https://arxiv.org/abs/1803.11417

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

arxiv:?https://arxiv.org/abs/1702.00307

Automatic Handgun Detection Alarm in Videos Using Deep Learning

arxiv:?https://arxiv.org/abs/1702.05147
results:?https://github.com/SihamTabik/Pistol-Detection-in-Videos

Objects as context for part detection

arxiv:?https://arxiv.org/abs/1703.09529

Using Deep Networks for Drone Detection

intro: AVSS 2017
arxiv:?https://arxiv.org/abs/1706.05726

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

intro: ICCV 2017
arxiv:?https://arxiv.org/abs/1708.01642

Target Driven Instance Detection

arxiv:?https://arxiv.org/abs/1803.04610

DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion

arxiv:?https://arxiv.org/abs/1709.04577

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition

intro: ICCV 2017
arxiv:?https://arxiv.org/abs/1710.06288
github:?https://github.com/SeokjuLee/VPGNet

Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants

arxiv:?https://arxiv.org/abs/1711.05128

ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos

intro: WACV 2018
arxiv:?https://arxiv.org/abs/1801.02031

Deep Learning Object Detection Methods for Ecological Camera Trap Data

intro: Conference of Computer and Robot Vision. University of Guelph
arxiv:?https://arxiv.org/abs/1803.10842

EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection

arxiv:?https://arxiv.org/abs/1806.05525

Towards End-to-End Lane Detection: an Instance Segmentation Approach

arxiv:?https://arxiv.org/abs/1802.05591

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

intro: BMVC 2018
project page:?https://gaochen315.github.io/iCAN/
arxiv:?https://arxiv.org/abs/1808.10437
github:?https://github.com/vt-vl-lab/iCAN

Densely Supervised Grasp Detector (DSGD)

https://arxiv.org/abs/1810.03962

Object Proposal

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

arxiv:?http://arxiv.org/abs/1510.04445
github:?https://github.com/aghodrati/deepproposal

Scale-aware Pixel-wise Object Proposal Networks

intro: IEEE Transactions on Image Processing
arxiv:?http://arxiv.org/abs/1601.04798

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

intro: BMVC 2016. AttractioNet
arxiv:?https://arxiv.org/abs/1606.04446
github:?https://github.com/gidariss/AttractioNet

Learning to Segment Object Proposals via Recursive Neural Networks

arxiv:?https://arxiv.org/abs/1612.01057

Learning Detection with Diverse Proposals

intro: CVPR 2017
keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse Proposals (LDDP)
arxiv:?https://arxiv.org/abs/1704.03533

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

keywords: product detection
arxiv:?https://arxiv.org/abs/1704.06752

Improving Small Object Proposals for Company Logo Detection

intro: ICMR 2017
arxiv:?https://arxiv.org/abs/1704.08881

Open Logo Detection Challenge

intro: BMVC 2018
keywords: QMUL-OpenLogo
project page:?https://qmul-openlogo.github.io/
arxiv:?https://arxiv.org/abs/1807.01964

Localization

Beyond Bounding Boxes: Precise Localization of Objects in Images

intro: PhD Thesis
homepage:?http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.html
phd-thesis:?http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.pdf
github(“SDS using hypercolumns”):?https://github.com/bharath272/sds

Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

arxiv:?http://arxiv.org/abs/1503.00949

Weakly Supervised Object Localization Using Size Estimates

arxiv:?http://arxiv.org/abs/1608.04314

Active Object Localization with Deep Reinforcement Learning

intro: ICCV 2015
keywords: Markov Decision Process
arxiv:?https://arxiv.org/abs/1511.06015

Localizing objects using referring expressions

intro: ECCV 2016
keywords: LSTM, multiple instance learning (MIL)
paper:?http://www.umiacs.umd.edu/~varun/files/refexp-ECCV16.pdf
github:?https://github.com/varun-nagaraja/referring-expressions

LocNet: Improving Localization Accuracy for Object Detection

intro: CVPR 2016 oral
arxiv:?http://arxiv.org/abs/1511.07763
github:?https://github.com/gidariss/LocNet

Learning Deep Features for Discriminative Localization

homepage:?http://cnnlocalization.csail.mit.edu/
arxiv:?http://arxiv.org/abs/1512.04150
github(Tensorflow):?https://github.com/jazzsaxmafia/Weakly_detector
github:?https://github.com/metalbubble/CAM
github:?https://github.com/tdeboissiere/VGG16CAM-keras

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

intro: ECCV 2016
project page:?http://www.di.ens.fr/willow/research/contextlocnet/
arxiv:?http://arxiv.org/abs/1609.04331
github:?https://github.com/vadimkantorov/contextlocnet

Ensemble of Part Detectors for Simultaneous Classification and Localization

arxiv:?https://arxiv.org/abs/1705.10034

STNet: Selective Tuning of Convolutional Networks for Object Localization

arxiv:?https://arxiv.org/abs/1708.06418

Soft Proposal Networks for Weakly Supervised Object Localization

intro: ICCV 2017
arxiv:?https://arxiv.org/abs/1709.01829

Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN

intro: ACM MM 2017
arxiv:?https://arxiv.org/abs/1709.08295

Tutorials / Talks

Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection

slides:?http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf

Towards Good Practices for Recognition & Detection

intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
slides:?http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf

Work in progress: Improving object detection and instance segmentation for small objects

https://docs.google.com/presentation/d/1OTfGn6mLe1VWE8D0q6Tu_WwFTSoLGd4OF8WCYnOWcVo/edit#slide=id.g37418adc7a_0_229

Object Detection with Deep Learning: A Review

arxiv:?https://arxiv.org/abs/1807.05511

Projects

Detectron

intro: FAIR’s research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
github:?https://github.com/facebookresearch/Detectron

TensorBox: a simple framework for training neural networks to detect objects in images

intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
github:?https://github.com/Russell91/TensorBox

Object detection in torch: Implementation of some object detection frameworks in torch

github:?https://github.com/fmassa/object-detection.torch

Using DIGITS to train an Object Detection network

github:?https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md

FCN-MultiBox Detector

intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
github:?https://github.com/teaonly/FMD.torch

KittiBox: A car detection model implemented in Tensorflow.

keywords: MultiNet
intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
github:?https://github.com/MarvinTeichmann/KittiBox

Deformable Convolutional Networks + MST + Soft-NMS

github:?https://github.com/bharatsingh430/Deformable-ConvNets

How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow

blog:?https://towardsdatascience.com/how-to-build-a-real-time-hand-detector-using-neural-networks-ssd-on-tensorflow-d6bac0e4b2ce
github:?https://github.com//victordibia/handtracking

Metrics for object detection

intro: Most popular metrics used to evaluate object detection algorithms
github:?https://github.com/rafaelpadilla/Object-Detection-Metrics

Leaderboard

Detection Results: VOC2012

intro: Competition “comp4” (train on additional data)
homepage:?http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

Tools

BeaverDam: Video annotation tool for deep learning training labels

https://github.com/antingshen/BeaverDam

Blogs

Convolutional Neural Networks for Object Detection

http://rnd.azoft.com/convolutional-neural-networks-object-detection/

Introducing automatic object detection to visual search (Pinterest)

keywords: Faster R-CNN
blog:?https://engineering.pinterest.com/blog/introducing-automatic-object-detection-visual-search
demo:?https://engineering.pinterest.com/sites/engineering/files/Visual Search V1 - Video.mp4
review:?https://news.developer.nvidia.com/pinterest-introduces-the-future-of-visual-search/?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D

Deep Learning for Object Detection with DIGITS

blog:?https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/

Analyzing The Papers Behind Facebook’s Computer Vision Approach

keywords: DeepMask, SharpMask, MultiPathNet
blog:?https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook’s-Computer-Vision-Approach/

Easily Create High Quality Object Detectors with Deep Learning

intro: dlib v19.2
blog:?http://blog.dlib.net/2016/10/easily-create-high-quality-object.html

How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit

blog:?https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/
github:?https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN

Object Detection in Satellite Imagery, a Low Overhead Approach

part 1:?https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7#.2csh4iwx9
part 2:?https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92#.f9b7dgf64

You Only Look Twice?—?Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks

part 1:?https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571#.fmmi2o3of
part 2:?https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588#.nwzarsz1t

Faster R-CNN Pedestrian and Car Detection

blog:?https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
ipn:?https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0#file-demo_faster_rcnn_notebook-ipynb
github:?https://github.com/bigsnarfdude/Faster-RCNN_TF

Small U-Net for vehicle detection

blog:?https://medium.com/@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6#.md4u80kad

Region of interest pooling explained

blog:?https://deepsense.io/region-of-interest-pooling-explained/
github:?https://github.com/deepsense-io/roi-pooling

Supercharge your Computer Vision models with the TensorFlow Object Detection API

blog:?https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
github:?https://github.com/tensorflow/models/tree/master/object_detection

Understanding SSD MultiBox?—?Real-Time Object Detection In Deep Learning

https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab

One-shot object detection

http://machinethink.net/blog/object-detection/

An overview of object detection: one-stage methods

https://www.jeremyjordan.me/object-detection-one-stage/

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目标检测

Object Detection(目标检测神文)

文章目錄

Papers

Deep Neural Networks for Object Detection

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

Fast R-CNN

Fast R-CNN

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

R-CNN minus R

Faster R-CNN in MXNet with distributed implementation and data parallelization

Contextual Priming and Feedback for Faster R-CNN

An Implementation of Faster RCNN with Study for Region Sampling

Interpretable R-CNN

Light-Head R-CNN

Light-Head R-CNN: In Defense of Two-Stage Object Detector

Cascade R-CNN: Delving into High Quality Object Detection

MultiBox

Scalable Object Detection using Deep Neural Networks

Scalable, High-Quality Object Detection

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

Object Detectors Emerge in Deep Scene CNNs

segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

Object Detection Networks on Convolutional Feature Maps

Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

DeepBox: Learning Objectness with Convolutional Networks

MR-CNN

Object detection via a multi-region & semantic segmentation-aware CNN model

YOLO

You Only Look Once: Unified, Real-Time Object Detection

darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++

Start Training YOLO with Our Own Data

YOLO: Core ML versus MPSNNGraph

TensorFlow YOLO object detection on Android

Computer Vision in iOS – Object Detection

YOLOv2

YOLO9000: Better, Faster, Stronger

darknet_scripts

Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2

LightNet: Bringing pjreddie’s DarkNet out of the shadows

YOLO v2 Bounding Box Tool

YOLOv3

YOLOv3: An Incremental Improvement

AttentionNet: Aggregating Weak Directions for Accurate Object Detection

DenseBox

DenseBox: Unifying Landmark Localization with End to End Object Detection

SSD

SSD: Single Shot MultiBox Detector

DSSD

DSSD : Deconvolutional Single Shot Detector

Enhancement of SSD by concatenating feature maps for object detection

Context-aware Single-Shot Detector

Feature-Fused SSD: Fast Detection for Small Objects

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

Weaving Multi-scale Context for Single Shot Detector

ESSD

Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network

Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection

MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects

Inside-Outside Net (ION)

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Adaptive Object Detection Using Adjacency and Zoom Prediction

G-CNN: an Iterative Grid Based Object Detector

Factors in Finetuning Deep Model for object detection

Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution

We don’t need no bounding-boxes: Training object class detectors using only human verification

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

A MultiPath Network for Object Detection

CRAFT

CRAFT Objects from Images

OHEM

Training Region-based Object Detectors with Online Hard Example Mining