0001，

object-detection

[TOC]

This is a list of awesome articles about object detection. If you want to read the paper according to time, you can refer to .

• R-CNN
• Fast R-CNN
• Faster R-CNN
• Mask R-CNN
• Light-Head R-CNN
• Cascade R-CNN
• SPP-Net
• YOLO
• YOLOv2
• YOLOv3
• YOLT
• SSD
• DSSD
• FSSD
• ESSD
• MDSSD
• Pelee
• Fire SSD
• R-FCN
• FPN
• DSOD
• RetinaNet
• MegDet
• RefineNet
• DetNet
• SSOD
• CornerNet
• M2Det
• 3D Object Detection
• ZSD（Zero-Shot Object Detection）
• OSD（One-Shot object Detection）
• Weakly Supervised Object Detection
• Softer-NMS
• 2018
• 2019
• Other

Based on handong1587's github:

 

Survey

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

• intro: awesome

• arXiv:

《Deep Learning for Generic Object Detection: A Survey》

• intro: Submitted to IJCV 2018
• arXiv:

 

Papers&Codes

 

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

• intro: R-CNN
• arxiv:
• supp:
• slides:
• slides:
• github:
• notes:
• caffe-pr("Make R-CNN the Caffe detection example"):

 

Fast R-CNN

Fast R-CNN

• arxiv:
• slides:
• github:
• github(COCO-branch):
• webcam demo:
• notes:
• notes:
• github("Fast R-CNN in MXNet"):
• github:
• github:
• github:

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

• intro: CVPR 2017
• arxiv:
• paper:
• github(Caffe):

 

Faster R-CNN

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

• intro: NIPS 2015
• arxiv:
• gitxiv:
• slides:
• github(official, Matlab):
• github(Caffe):
• github(MXNet):
• github(PyTorch--recommend):
• github:
• github(Torch)::
• github(Torch)::
• github(TensorFlow):
• github(TensorFlow):
• github(C++ demo):
• github(Keras):
• github:
• github(C++):

R-CNN minus R

• intro: BMVC 2015
• arxiv:

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

• github:

Contextual Priming and Feedback for Faster R-CNN

• intro: ECCV 2016. Carnegie Mellon University
• paper:
• poster:

An Implementation of Faster RCNN with Study for Region Sampling

• intro: Technical Report, 3 pages. CMU
• arxiv:
• github:
• github:

Interpretable R-CNN

• intro: North Carolina State University & Alibaba
• keywords: AND-OR Graph (AOG)
• arxiv:

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

• intro: CVPR 2018. ETH Zurich & ESAT/PSI
• arxiv:

 

Mask R-CNN

• arxiv:
• github(Keras):
• github(Caffe2):
• github(Pytorch):
• github(MXNet):
• github(Chainer):

 

Light-Head R-CNN

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

• intro: Tsinghua University & Megvii Inc
• arxiv:
• github(offical):
• github:

 

Cascade R-CNN

Cascade R-CNN: Delving into High Quality Object Detection

• arxiv:
• github:

 

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

• intro: ECCV 2014 / TPAMI 2015
• arxiv:
• github:
• notes:

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:
• arxiv:

Object Detectors Emerge in Deep Scene CNNs

• intro: ICLR 2015
• arxiv:
• paper:
• paper:
• slides:

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

• intro: CVPR 2015
• project(code+data):
• arxiv:
• github:

Object Detection Networks on Convolutional Feature Maps

• intro: TPAMI 2015
• keywords: NoC
• arxiv:

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

• arxiv:
• slides:
• github:

DeepBox: Learning Objectness with Convolutional Networks

• keywords: DeepBox
• arxiv:
• github:

 

YOLO

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

• arxiv:
• code:
• github:
• blog:
• slides:
• reddit:
• github:
• github:
• github:
• github:
• github:
• github:
• github:
• github:

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

• blog:
• github:

Start Training YOLO with Our Own Data

• intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
• blog:
• github:

YOLO: Core ML versus MPSNNGraph

• intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
• blog:
• github:

TensorFlow YOLO object detection on Android

• intro: Real-time object detection on Android using the YOLO network with TensorFlow
• github:

Computer Vision in iOS – Object Detection

• blog:
• github:

 

YOLOv2

YOLO9000: Better, Faster, Stronger

• arxiv:
• code:
• github(Chainer):
• github(Keras):
• github(PyTorch):
• github(Tensorflow):
• github(Windows):
• github:
• github:
• github(TensorFlow):
• github(Keras):
• github(Keras):
• github(TensorFlow):

darknet_scripts

• intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
• github:

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

• github:

LightNet: Bringing pjreddie's DarkNet out of the shadows

YOLO v2 Bounding Box Tool

• intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
• github:

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

• intro: LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.
• arxiv:

Object detection at 200 Frames Per Second

• intro: faster than Tiny-Yolo-v2
• arxiv:

Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras

• intro: YOLE--Object Detection in Neuromorphic Cameras
• arxiv:

OmniDetector: With Neural Networks to Bounding Boxes

• intro: a person detector on n fish-eye images of indoor scenes（NIPS 2018）
• arxiv:
• datasets:

 

YOLOv3

YOLOv3: An Incremental Improvement

• arxiv:
• paper:
• code:
• github(Official):
• github:
• github:
• github:
• github:
• github:
• github:
• github:
• github:
• github:
• github:
• github:

 

YOLT

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

• intro: Small Object Detection

• arxiv:

• github:

 

SSD

SSD: Single Shot MultiBox Detector

• intro: ECCV 2016 Oral
• arxiv:
• paper:
• slides:
• github(Official):
• video:
• github:
• github:
• github:
• github:
• github:
• github(Caffe):

What's the diffience in performance between this new code you pushed and the previous code? #327

 

DSSD

DSSD : Deconvolutional Single Shot Detector

• intro: UNC Chapel Hill & Amazon Inc
• arxiv:
• github:
• github:
• demo:

Enhancement of SSD by concatenating feature maps for object detection

• intro: rainbow SSD (R-SSD)
• arxiv:

Context-aware Single-Shot Detector

• keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
• arxiv:

Feature-Fused SSD: Fast Detection for Small Objects

 

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

Weaving Multi-scale Context for Single Shot Detector

• intro: WeaveNet
• keywords: fuse multi-scale information
• arxiv:

 

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

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

• arxiv:

 

Pelee

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

• intro: (ICLR 2018 workshop track)

• arxiv:

• github:

 

Fire SSD

Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device

• intro:low cost, fast speed and high mAP on factor edge computing devices

• arxiv:

 

R-FCN

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

• arxiv:
• github:
• github(MXNet):
• github:
• github:
• github:
• github:

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

Recycle deep features for better object detection

• arxiv:

 

FPN

Feature Pyramid Networks for Object Detection

• intro: Facebook AI Research
• arxiv:

Action-Driven Object Detection with Top-Down Visual Attentions

• arxiv:

Beyond Skip Connections: Top-Down Modulation for Object Detection

• intro: CMU & UC Berkeley & Google Research
• arxiv:

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

• intro: Inha University
• arxiv:

Attentional Network for Visual Object Detection

• intro: University of Maryland & Mitsubishi Electric Research Laboratories
• arxiv:

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:

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

• intro: ICCV 2017 (poster)
• arxiv:

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

• intro: CVPR 2017
• arxiv:

Spatial Memory for Context Reasoning in Object Detection

• arxiv:

Accurate Single Stage Detector Using Recurrent Rolling Convolution

• intro: CVPR 2017. SenseTime
• keywords: Recurrent Rolling Convolution (RRC)
• arxiv:
• github:

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

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:

Point Linking Network for Object Detection

• intro: Point Linking Network (PLN)
• arxiv:

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

• intro: CVPR 2017
• arxiv:
• github:

Mimicking Very Efficient Network for Object Detection

• intro: CVPR 2017. SenseTime & Beihang University
• paper:

Residual Features and Unified Prediction Network for Single Stage Detection

Deformable Part-based Fully Convolutional Network for Object Detection

• intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
• arxiv:

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

• intro: ICCV 2017
• arxiv:

Recurrent Scale Approximation for Object Detection in CNN

• intro: ICCV 2017
• keywords: Recurrent Scale Approximation (RSA)
• arxiv:
• github:

 

DSOD

DSOD: Learning Deeply Supervised Object Detectors from Scratch

• intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
• arxiv:
• github:
• github:
• github:

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

• arxiv:
• github:

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

• intro: BMVC 2018
• arXiv:

Object Detection from Scratch with Deep Supervision

• intro: This is an extended version of DSOD
• arXiv:

 

RetinaNet

Focal Loss for Dense Object Detection

• intro: ICCV 2017 Best student paper award. Facebook AI Research
• keywords: RetinaNet
• arxiv:

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

• intro: ICCV 2017
• arxiv:

Incremental Learning of Object Detectors without Catastrophic Forgetting

• intro: ICCV 2017. Inria
• arxiv:

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

• intro: NTU, Singapore & Amazon
• keywords: multi-instance multi-label domain adaption learning framework
• arxiv:

 

MegDet

MegDet: A Large Mini-Batch Object Detector

• intro: Peking University & Tsinghua University & Megvii Inc
• arxiv:

Receptive Field Block Net for Accurate and Fast Object Detection

• intro: RFBNet
• arxiv:
• github:

An Analysis of Scale Invariance in Object Detection - SNIP

• arxiv:
• github:

Feature Selective Networks for Object Detection

Learning a Rotation Invariant Detector with Rotatable Bounding Box

• arxiv:
• github:

Scalable Object Detection for Stylized Objects

• intro: Microsoft AI & Research Munich
• arxiv:

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

• arxiv:
• github:

Deep Regionlets for Object Detection

• keywords: region selection network, gating network
• arxiv:

Training and Testing Object Detectors with Virtual Images

• intro: IEEE/CAA Journal of Automatica Sinica
• arxiv:

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:

Spot the Difference by Object Detection

• intro: Tsinghua University & JD Group
• arxiv:

Localization-Aware Active Learning for Object Detection

• arxiv:

Object Detection with Mask-based Feature Encoding

• arxiv:

LSTD: A Low-Shot Transfer Detector for Object Detection

• intro: AAAI 2018
• arxiv:

Pseudo Mask Augmented Object Detection

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Learning Region Features for Object Detection

• intro: Peking University & MSRA
• arxiv:

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

• intro: Singapore Management University & Zhejiang University
• arxiv:

Object Detection for Comics using Manga109 Annotations

• intro: University of Tokyo & National Institute of Informatics, Japan
• arxiv:

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

• arxiv:

Transferring Common-Sense Knowledge for Object Detection

• arxiv:

Multi-scale Location-aware Kernel Representation for Object Detection

• intro: CVPR 2018
• arxiv:
• github:

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

• intro: National University of Defense Technology
• arxiv:

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

• arxiv:

 

RefineNet

Single-Shot Refinement Neural Network for Object Detection

• intro: CVPR 2018

• arxiv:

• github:

• github:

• github:

• github:

 

DetNet

DetNet: A Backbone network for Object Detection

• intro: Tsinghua University & Face++
• arxiv:

 

SSOD

Self-supervisory Signals for Object Discovery and Detection

• Google Brain
• arxiv:

 

CornerNet

CornerNet: Detecting Objects as Paired Keypoints

• intro: ECCV 2018
• arXiv:
• github:

 

M2Det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

• intro: AAAI 2019
• arXiv:
• github:

 

3D Object Detection

3D Backbone Network for 3D Object Detection

• arXiv:

LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs

• arxiv:
• github:

 

ZSD

Zero-Shot Detection

• intro: Australian National University
• keywords: YOLO
• arxiv:

Zero-Shot Object Detection

• arxiv:

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

• arxiv:

Zero-Shot Object Detection by Hybrid Region Embedding

• arxiv:

 

OSD

One-Shot Object Detection

RepMet: Representative-based metric learning for classification and one-shot object detection

• intro: IBM Research AI
• arxiv:
• github: TODO

 

Weakly Supervised Object Detection

Weakly Supervised Object Detection in Artworks

• intro: ECCV 2018 Workshop Computer Vision for Art Analysis
• arXiv:
• Datasets:

Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

• intro: CVPR 2018
• arXiv:
• homepage:
• paper:
• github:

 

Softer-NMS

《Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection》

• intro: CMU & Face++
• arXiv:
• github:

 

2019

Object Detection based on Region Decomposition and Assembly

• intro: AAAI 2019

• arXiv:

Bottom-up Object Detection by Grouping Extreme and Center Points

• intro: one stage 43.2% on COCO test-dev
• arXiv:
• github:

ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

• intro: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

• arXiv:

Consistent Optimization for Single-Shot Object Detection

• intro: improves RetinaNet from 39.1 AP to 40.1 AP on COCO datase

• arXiv:

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

• arXiv:

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

• arXiv:
• github:

Region Proposal by Guided Anchoring

• intro: CUHK - SenseTime Joint Lab
• arXiv:

Scale-Aware Trident Networks for Object Detection

• intro: mAP of 48.4 on the COCO dataset
• arXiv:

 

2018

Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions

• arXiv:

Strong-Weak Distribution Alignment for Adaptive Object Detection

• arXiv:

AutoFocus: Efficient Multi-Scale Inference

• intro: AutoFocus obtains an mAP of 47.9% (68.3% at 50% overlap) on the COCO test-dev set while processing 6.4 images per second on a Titan X (Pascal) GPU
• arXiv:

NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection

• intro: Google Could
• arXiv:

SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection

• intro: UC Berkeley
• arXiv:

Grid R-CNN

• intro: SenseTime
• arXiv:

Deformable ConvNets v2: More Deformable, Better Results

• intro: Microsoft Research Asia

• arXiv:

Anchor Box Optimization for Object Detection

• intro: Microsoft Research
• arXiv:

Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects

• intro:

NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection

• arXiv:

Learning RoI Transformer for Detecting Oriented Objects in Aerial Images

• arXiv:

Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

• intro: Microsoft Research Asia
• arXiv:

Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection

• arXiv:

Gradient Harmonized Single-stage Detector

• intro: AAAI 2019
• arXiv:

CFENet: Object Detection with Comprehensive Feature Enhancement Module

• intro: ACCV 2018
• github:

DeRPN: Taking a further step toward more general object detection

• intro: AAAI 2019
• arXiv:
• github:

Hybrid Knowledge Routed Modules for Large-scale Object Detection

• intro: Sun Yat-Sen University & Huawei Noah’s Ark Lab
• arXiv:
• github:

《Receptive Field Block Net for Accurate and Fast Object Detection》

• intro: ECCV 2018
• arXiv:
• github:

Deep Feature Pyramid Reconfiguration for Object Detection

• intro: ECCV 2018
• arXiv:

Unsupervised Hard Example Mining from Videos for Improved Object Detection

• intro: ECCV 2018
• arXiv:

Acquisition of Localization Confidence for Accurate Object Detection

• intro: ECCV 2018
• arXiv:
• github:

Toward Scale-Invariance and Position-Sensitive Region Proposal Networks

• intro: ECCV 2018
• arXiv:

MetaAnchor: Learning to Detect Objects with Customized Anchors

• arxiv:

Relation Network for Object Detection

• intro: CVPR 2018
• arxiv:
• github:

Quantization Mimic: Towards Very Tiny CNN for Object Detection

• Tsinghua University1 & The Chinese University of Hong Kong2 &SenseTime3
• arxiv:

Learning Rich Features for Image Manipulation Detection

• intro: CVPR 2018 Camera Ready
• arxiv:

SNIPER: Efficient Multi-Scale Training

• arxiv:
• github:

Soft Sampling for Robust Object Detection

• intro: the robustness of object detection under the presence of missing annotations
• arxiv:

Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria

• intro: TNNLS 2018
• arxiv:
• code:

 

Other

R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos

• arxiv:
• youtube:

 

Detection Toolbox

• : Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including . It is written in Python and powered by the deep learning framework.

• : Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

• : mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by .

 

0002，

deep learning object detection

A paper list of object detection using deep learning. I worte this page with reference to and searching and searching..

Last updated: 2019/03/18

 

Update log

2018/9/18 - update all of recent papers and make some diagram about history of object detection using deep learning. 2018/9/26 - update codes of papers. (official and unofficial)

2018/october - update 5 papers and performance table.

2018/november - update 9 papers.

2018/december - update 8 papers and and performance table and add new diagram(2019 version!!).

2019/january - update 4 papers and and add commonly used datasets.

2019/february - update 3 papers.

2019/march - update figure and code links.

 

Table of Contents

• Papers

 

Paper list from 2014 to now(2019)

The part highlighted with red characters means papers that i think "must-read". However, it is my personal opinion and other papers are important too, so I recommend to read them if you have time.

 

Performance table

FPS(Speed) index is related to the hardware spec(e.g. CPU, GPU, RAM, etc), so it is hard to make an equal comparison. The solution is to measure the performance of all models on hardware with equivalent specifications, but it is very difficult and time consuming.

Detector	VOC07 (mAP@IoU=0.5)	VOC12 (mAP@IoU=0.5)	COCO (mAP@IoU=0.5:0.95)	Published In
R-CNN	58.5	-	-	CVPR'14
SPP-Net	59.2	-	-	ECCV'14
MR-CNN	78.2 (07+12)	73.9 (07+12)	-	ICCV'15
Fast R-CNN	70.0 (07+12)	68.4 (07++12)	19.7	ICCV'15
Faster R-CNN	73.2 (07+12)	70.4 (07++12)	21.9	NIPS'15
YOLO v1	66.4 (07+12)	57.9 (07++12)	-	CVPR'16
G-CNN	66.8	66.4 (07+12)	-	CVPR'16
AZNet	70.4	-	22.3	CVPR'16
ION	80.1	77.9	33.1	CVPR'16
HyperNet	76.3 (07+12)	71.4 (07++12)	-	CVPR'16
OHEM	78.9 (07+12)	76.3 (07++12)	22.4	CVPR'16
MPN	-	-	33.2	BMVC'16
SSD	76.8 (07+12)	74.9 (07++12)	31.2	ECCV'16
GBDNet	77.2 (07+12)	-	27.0	ECCV'16
CPF	76.4 (07+12)	72.6 (07++12)	-	ECCV'16
R-FCN	79.5 (07+12)	77.6 (07++12)	29.9	NIPS'16
DeepID-Net	69.0	-	-	PAMI'16
NoC	71.6 (07+12)	68.8 (07+12)	27.2	TPAMI'16
DSSD	81.5 (07+12)	80.0 (07++12)	33.2	arXiv'17
TDM	-	-	37.3	CVPR'17
FPN	-	-	36.2	CVPR'17
YOLO v2	78.6 (07+12)	73.4 (07++12)	-	CVPR'17
RON	77.6 (07+12)	75.4 (07++12)	27.4	CVPR'17
DeNet	77.1 (07+12)	73.9 (07++12)	33.8	ICCV'17
CoupleNet	82.7 (07+12)	80.4 (07++12)	34.4	ICCV'17
RetinaNet	-	-	39.1	ICCV'17
DSOD	77.7 (07+12)	76.3 (07++12)	-	ICCV'17
SMN	70.0	-	-	ICCV'17
Light-Head R-CNN	-	-	41.5	arXiv'17
YOLO v3	-	-	33.0	arXiv'18
SIN	76.0 (07+12)	73.1 (07++12)	23.2	CVPR'18
STDN	80.9 (07+12)	-	-	CVPR'18
RefineDet	83.8 (07+12)	83.5 (07++12)	41.8	CVPR'18
SNIP	-	-	45.7	CVPR'18
Relation-Network	-	-	32.5	CVPR'18
Cascade R-CNN	-	-	42.8	CVPR'18
MLKP	80.6 (07+12)	77.2 (07++12)	28.6	CVPR'18
Fitness-NMS	-	-	41.8	CVPR'18
RFBNet	82.2 (07+12)	-	-	ECCV'18
CornerNet	-	-	42.1	ECCV'18
PFPNet	84.1 (07+12)	83.7 (07++12)	39.4	ECCV'18
Pelee	70.9 (07+12)	-	-	NIPS'18
HKRM	78.8 (07+12)	-	37.8	NIPS'18
M2Det	-	-	44.2	AAAI'19
R-DAD	81.2 (07++12)	82.0 (07++12)	43.1	AAAI'19

 

2014

• [R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik | [CVPR' 14] |

• [OverFeat] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | Pierre Sermanet, et al. | [ICLR' 14] |

• [MultiBox] Scalable Object Detection using Deep Neural Networks | Dumitru Erhan, et al. | [CVPR' 14] |

• [SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | Kaiming He, et al. | [ECCV' 14] |

 

2015

• Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction | Yuting Zhang, et. al. | [CVPR' 15] |

• [MR-CNN] Object detection via a multi-region & semantic segmentation-aware CNN model | Spyros Gidaris, Nikos Komodakis | [ICCV' 15] |

• [DeepBox] DeepBox: Learning Objectness with Convolutional Networks | Weicheng Kuo, Bharath Hariharan, Jitendra Malik | [ICCV' 15] |

• [AttentionNet] AttentionNet: Aggregating Weak Directions for Accurate Object Detection | Donggeun Yoo, et al. | [ICCV' 15] |

• [Fast R-CNN] Fast R-CNN | Ross Girshick | [ICCV' 15] |

• [DeepProposal] DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | Amir Ghodrati, et al. | [ICCV' 15] |

• [Faster R-CNN, RPN] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | Shaoqing Ren, et al. | [NIPS' 15] |

 

2016

• [YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | Joseph Redmon, et al. | [CVPR' 16] |

• [G-CNN] G-CNN: an Iterative Grid Based Object Detector | Mahyar Najibi, et al. | [CVPR' 16] |

• [AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | Yongxi Lu, Tara Javidi. | [CVPR' 16] |

• [ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | Sean Bell, et al. | [CVPR' 16] |

• [HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | Tao Kong, et al. | [CVPR' 16] |

• [OHEM] Training Region-based Object Detectors with Online Hard Example Mining | Abhinav Shrivastava, et al. | [CVPR' 16] |

• [CRAPF] CRAFT Objects from Images | Bin Yang, et al. | [CVPR' 16] |

• [MPN] A MultiPath Network for Object Detection | Sergey Zagoruyko, et al. | [BMVC' 16] |

• [SSD] SSD: Single Shot MultiBox Detector | Wei Liu, et al. | [ECCV' 16] |

• [GBDNet] Crafting GBD-Net for Object Detection | Xingyu Zeng, et al. | [ECCV' 16] |

• [CPF] Contextual Priming and Feedback for Faster R-CNN | Abhinav Shrivastava and Abhinav Gupta | [ECCV' 16] |

• [MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | Zhaowei Cai, et al. | [ECCV' 16] |

• [R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | Jifeng Dai, et al. | [NIPS' 16] |

• [PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | Kye-Hyeon Kim, et al. | [NIPSW' 16] |

• [DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | Wanli Ouyang, et al. | [PAMI' 16] |

• [NoC] Object Detection Networks on Convolutional Feature Maps | Shaoqing Ren, et al. | [TPAMI' 16] |

 

2017

• [DSSD] DSSD : Deconvolutional Single Shot Detector | Cheng-Yang Fu1, et al. | [arXiv' 17] |

• [TDM] Beyond Skip Connections: Top-Down Modulation for Object Detection | Abhinav Shrivastava, et al. | [CVPR' 17] |

• [FPN] Feature Pyramid Networks for Object Detection | Tsung-Yi Lin, et al. | [CVPR' 17] |

• [YOLO v2] YOLO9000: Better, Faster, Stronger | Joseph Redmon, Ali Farhadi | [CVPR' 17] |

• [RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | Tao Kong, et al. | [CVPR' 17] |

• [RSA] Recurrent Scale Approximation for Object Detection in CNN | Yu Liu, et al. | | [ICCV' 17] |

• [DCN] Deformable Convolutional Networks | Jifeng Dai, et al. | [ICCV' 17] |

• [DeNet] DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | Lachlan Tychsen-Smith, Lars Petersson | [ICCV' 17] |

• [CoupleNet] CoupleNet: Coupling Global Structure with Local Parts for Object Detection | Yousong Zhu, et al. | [ICCV' 17] |

• [RetinaNet] Focal Loss for Dense Object Detection | Tsung-Yi Lin, et al. | [ICCV' 17] |

• [Mask R-CNN] Mask R-CNN | Kaiming He, et al. | [ICCV' 17] |

• [DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | Zhiqiang Shen, et al. | [ICCV' 17] |

• [SMN] Spatial Memory for Context Reasoning in Object Detection | Xinlei Chen, Abhinav Gupta | [ICCV' 17] |

• [Light-Head R-CNN] Light-Head R-CNN: In Defense of Two-Stage Object Detector | Zeming Li, et al. | [arXiv' 17] |

• [Soft-NMS] Improving Object Detection With One Line of Code | Navaneeth Bodla, et al. | [ICCV' 17] |

 

2018

• [YOLO v3] YOLOv3: An Incremental Improvement | Joseph Redmon, Ali Farhadi | [arXiv' 18] |

• [ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | Hongyang Li, et al. | [IJCV' 18] |

• [SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | Yong Liu, et al. | [CVPR' 18] |

• [STDN] Scale-Transferrable Object Detection | Peng Zhou, et al. | [CVPR' 18] |

• [RefineDet] Single-Shot Refinement Neural Network for Object Detection | Shifeng Zhang, et al. | [CVPR' 18] |

• [MegDet] MegDet: A Large Mini-Batch Object Detector | Chao Peng, et al. | [CVPR' 18] |

• [DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | Yuhua Chen, et al. | [CVPR' 18] |

• [SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | Bharat Singh, Larry S. Davis | [CVPR' 18] |

• [Relation-Network] Relation Networks for Object Detection | Han Hu, et al. | [CVPR' 18] |

• [Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | Zhaowei Cai, et al. | [CVPR' 18] |

• Finding Tiny Faces in the Wild with Generative Adversarial Network | Yancheng Bai, et al. | [CVPR' 18] |

• [MLKP] Multi-scale Location-aware Kernel Representation for Object Detection | Hao Wang, et al. | [CVPR' 18] |

• Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation | Naoto Inoue, et al. | [CVPR' 18] |

• [Fitness NMS] Improving Object Localization with Fitness NMS and Bounded IoU Loss | Lachlan Tychsen-Smith, Lars Petersson. | [CVPR' 18] |

• [STDnet] STDnet: A ConvNet for Small Target Detection | Brais Bosquet, et al. | [BMVC' 18] |

• [RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | Songtao Liu, et al. | [ECCV' 18] |

• Zero-Annotation Object Detection with Web Knowledge Transfer | Qingyi Tao, et al. | [ECCV' 18] |

• [CornerNet] CornerNet: Detecting Objects as Paired Keypoints | Hei Law, et al. | [ECCV' 18] |

• [PFPNet] Parallel Feature Pyramid Network for Object Detection | Seung-Wook Kim, et al. | [ECCV' 18] |

• [Softer-NMS] Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | Yihui He, et al. | [arXiv' 18] |

• [ShapeShifter] ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector | Shang-Tse Chen, et al. | [ECML-PKDD' 18] |

• [Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | Jun Wang, et al. | [NIPS' 18] |

• [HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | ChenHan Jiang, et al. | [NIPS' 18] |

• [MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | Tong Yang, et al. | [NIPS' 18] |

• [SNIPER] SNIPER: Efficient Multi-Scale Training | Bharat Singh, et al. | [NIPS' 18] |

 

2019

• [M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | Qijie Zhao, et al. | [AAAI' 19] |

• [R-DAD] Object Detection based on Region Decomposition and Assembly | Seung-Hwan Bae | [AAAI' 19] |

• [CAMOU] CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | Yang Zhang, et al. | [ICLR' 19] |

 

Dataset Papers

Statistics of commonly used object detection datasets. The Figure came from .

The papers related to datasets used mainly in Object Detection are as follows.

• [PASCAL VOC] The PASCAL Visual Object Classes (VOC) Challenge | Mark Everingham, et al. | [IJCV' 10] |

• [PASCAL VOC] The PASCAL Visual Object Classes Challenge: A Retrospective | Mark Everingham, et al. | [IJCV' 15] | |

• [ImageNet] ImageNet: A Large-Scale Hierarchical Image Database | Jia Deng, et al. | [CVPR' 09] |

• [ImageNet] ImageNet Large Scale Visual Recognition Challenge | Olga Russakovsky, et al. | [IJCV' 15] | |

• [COCO] Microsoft COCO: Common Objects in Context | Tsung-Yi Lin, et al. | [ECCV' 14] | |

• [Open Images] The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale | A Kuznetsova, et al. | [arXiv' 18] | |

 

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