转：awesome-object-detection

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 Date.

• 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: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

 

Survey

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

• intro: awesome

• arXiv: https://arxiv.org/abs/1809.03193

《Deep Learning for Generic Object Detection: A Survey》

• intro: Submitted to IJCV 2018
• arXiv: https://arxiv.org/abs/1809.02165

 

Papers&Codes

 

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

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(Caffe): https://github.com/rbgirshick/py-faster-rcnn
• github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn
• github(PyTorch--recommend): https://github.com//jwyang/faster-rcnn.pytorch
• github: https://github.com/mitmul/chainer-faster-rcnn
• github(Torch):: https://github.com/andreaskoepf/faster-rcnn.torch
• github(Torch):: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
• github(TensorFlow): https://github.com/smallcorgi/Faster-RCNN_TF
• github(TensorFlow): https://github.com/CharlesShang/TFFRCNN
• github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
• github(Keras): 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
• github: https://github.com/ruotianluo/pytorch-faster-rcnn

Interpretable R-CNN

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

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

 

Mask R-CNN

• arxiv: http://arxiv.org/abs/1703.06870
• github(Keras): https://github.com/matterport/Mask_RCNN
• github(Caffe2): https://github.com/facebookresearch/Detectron
• github(Pytorch): https://github.com/wannabeOG/Mask-RCNN
• github(MXNet): https://github.com/TuSimple/mx-maskrcnn
• github(Chainer): https://github.com/DeNA/Chainer_Mask_R-CNN

 

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(offical): 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

• arxiv: https://arxiv.org/abs/1712.00726
• github: https://github.com/zhaoweicai/cascade-rcnn

 

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/%CB%9Cwlouyang/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

 

YOLO

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

• arxiv: http://arxiv.org/abs/1506.02640
• code: https://pjreddie.com/darknet/yolov1/
• github: https://github.com/pjreddie/darknet
• blog: https://pjreddie.com/darknet/yolov1/
• 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/ https://pjreddie.com/darknet/yolov2/
• 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
• github(TensorFlow): https://github.com/KOD-Chen/YOLOv2-Tensorflow
• github(Keras): https://github.com/yhcc/yolo2
• github(Keras): https://github.com/experiencor/keras-yolo2
• github(TensorFlow): https://github.com/WojciechMormul/yolo2

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

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

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: https://arxiv.org/abs/1804.04606

Object detection at 200 Frames Per Second

• intro: faster than Tiny-Yolo-v2
• arxiv: https://arxiv.org/abs/1805.06361

Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras

• intro: YOLE--Object Detection in Neuromorphic Cameras
• arxiv:https://arxiv.org/abs/1805.07931

OmniDetector: With Neural Networks to Bounding Boxes

• intro: a person detector on n fish-eye images of indoor scenes（NIPS 2018）
• arxiv:https://arxiv.org/abs/1805.08503
• datasets:https://gitlab.com/omnidetector/omnidetector

 

YOLOv3

YOLOv3: An Incremental Improvement

• arxiv:https://arxiv.org/abs/1804.02767
• paper:https://pjreddie.com/media/files/papers/YOLOv3.pdf
• code: https://pjreddie.com/darknet/yolo/
• github(Official):https://github.com/pjreddie/darknet
• github:https://github.com/mystic123/tensorflow-yolo-v3
• github:https://github.com/experiencor/keras-yolo3
• github:https://github.com/qqwweee/keras-yolo3
• github:https://github.com/marvis/pytorch-yolo3
• github:https://github.com/ayooshkathuria/pytorch-yolo-v3
• github:https://github.com/ayooshkathuria/YOLO_v3_tutorial_from_scratch
• github:https://github.com/eriklindernoren/PyTorch-YOLOv3
• github:https://github.com/ultralytics/yolov3
• github:https://github.com/BobLiu20/YOLOv3_PyTorch
• github:https://github.com/andy-yun/pytorch-0.4-yolov3
• github:https://github.com/DeNA/PyTorch_YOLOv3

 

YOLT

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

• intro: Small Object Detection

• arxiv:https://arxiv.org/abs/1805.09512

• github:https://github.com/avanetten/yolt

 

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/%7Ewliu/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

https://arxiv.org/abs/1801.05918

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

https://arxiv.org/abs/1802.06488

 

MDSSD

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

• arxiv: https://arxiv.org/abs/1805.07009

 

Pelee

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

https://github.com/Robert-JunWang/Pelee

• intro: (ICLR 2018 workshop track)

• arxiv: https://arxiv.org/abs/1804.06882

• github: https://github.com/Robert-JunWang/Pelee

 

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:https://arxiv.org/abs/1806.05363

 

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

https://arxiv.org/abs/1712.01802

Recycle deep features for better object detection

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

 

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

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

https://arxiv.org/abs/1706.05274

Few-shot Object Detection

https://arxiv.org/abs/1706.08249

Yes-Net: An effective Detector Based on Global Information

https://arxiv.org/abs/1706.09180

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

https://arxiv.org/abs/1706.10217

Towards lightweight convolutional neural networks for object detection

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
• github:https://github.com/Windaway/DSOD-Tensorflow
• github:https://github.com/chenyuntc/dsod.pytorch

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

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

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

• intro: BMVC 2018
• arXiv: https://arxiv.org/abs/1807.11013

Object Detection from Scratch with Deep Supervision

• intro: This is an extended version of DSOD
• 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

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

https://arxiv.org/abs/1709.04347

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

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

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

• arxiv: https://arxiv.org/abs/1711.08189
• github: https://github.com/bharatsingh430/snip

Feature Selective Networks for Object Detection

https://arxiv.org/abs/1711.08879

Learning a Rotation Invariant Detector with Rotatable Bounding Box

• arxiv: https://arxiv.org/abs/1711.09405
• github: 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

Pseudo Mask Augmented Object Detection

https://arxiv.org/abs/1803.05858

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://arxiv.org/abs/1803.06799

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

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

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

 

RefineNet

Single-Shot Refinement Neural Network for Object Detection

• intro: CVPR 2018

• arxiv: https://arxiv.org/abs/1711.06897

• github: https://github.com/sfzhang15/RefineDet

• github: https://github.com/lzx1413/PytorchSSD

• github: https://github.com/ddlee96/RefineDet_mxnet

• github: https://github.com/MTCloudVision/RefineDet-Mxnet

 

DetNet

DetNet: A Backbone network for Object Detection

• intro: Tsinghua University & Face++
• arxiv: https://arxiv.org/abs/1804.06215

 

SSOD

Self-supervisory Signals for Object Discovery and Detection

• Google Brain
• arxiv:https://arxiv.org/abs/1806.03370

 

CornerNet

CornerNet: Detecting Objects as Paired Keypoints

• intro: ECCV 2018
• arXiv: https://arxiv.org/abs/1808.01244
• github: https://github.com/umich-vl/CornerNet

 

M2Det

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

• intro: AAAI 2019
• arXiv: https://arxiv.org/abs/1811.04533
• github: https://github.com/qijiezhao/M2Det

 

3D Object Detection

3D Backbone Network for 3D Object Detection

• arXiv: https://arxiv.org/abs/1901.08373

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

• arxiv: https://arxiv.org/abs/1805.04902
• github: https://github.com/CPFL/Autoware/tree/feature/cnn_lidar_detection

 

ZSD

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

• arxiv: https://arxiv.org/abs/1803.06049

Zero-Shot Object Detection by Hybrid Region Embedding

• arxiv: https://arxiv.org/abs/1805.06157

 

OSD

One-Shot Object Detection

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

• intro: IBM Research AI
• arxiv:https://arxiv.org/abs/1806.04728
• github: TODO

 

Weakly Supervised Object Detection

Weakly Supervised Object Detection in Artworks

• intro: ECCV 2018 Workshop Computer Vision for Art Analysis
• arXiv: https://arxiv.org/abs/1810.02569
• Datasets: https://wsoda.telecom-paristech.fr/downloads/dataset/IconArt_v1.zip

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

• intro: CVPR 2018
• arXiv: https://arxiv.org/abs/1803.11365
• homepage: https://naoto0804.github.io/cross_domain_detection/
• paper: http://openaccess.thecvf.com/content_cvpr_2018/html/Inoue_Cross-Domain_Weakly-Supervised_Object_CVPR_2018_paper.html
• github: https://github.com/naoto0804/cross-domain-detection

 

Softer-NMS

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

• intro: CMU & Face++
• arXiv: https://arxiv.org/abs/1809.08545
• github: https://github.com/yihui-he/softer-NMS

 

2019

Object Detection based on Region Decomposition and Assembly

• intro: AAAI 2019

• arXiv: https://arxiv.org/abs/1901.08225

Bottom-up Object Detection by Grouping Extreme and Center Points

• intro: one stage 43.2% on COCO test-dev
• arXiv: https://arxiv.org/abs/1901.08043
• github: https://github.com/xingyizhou/ExtremeNet

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: https://arxiv.org/abs/1901.07925

Consistent Optimization for Single-Shot Object Detection

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

• arXiv: https://arxiv.org/abs/1901.06563

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

• arXiv: https://arxiv.org/abs/1901.03796

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

• arXiv: https://arxiv.org/abs/1901.03353
• github: https://github.com/chengyangfu/retinamask

Region Proposal by Guided Anchoring

• intro: CUHK - SenseTime Joint Lab
• arXiv: https://arxiv.org/abs/1901.03278

Scale-Aware Trident Networks for Object Detection

• intro: mAP of 48.4 on the COCO dataset
• arXiv: https://arxiv.org/abs/1901.01892

 

2018

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

• arXiv: https://arxiv.org/abs/1812.11901

Strong-Weak Distribution Alignment for Adaptive Object Detection

• arXiv: https://arxiv.org/abs/1812.04798

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: https://arxiv.org/abs/1812.01600

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

• intro: Google Could
• arXiv: https://arxiv.org/abs/1812.00124

SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection

• intro: UC Berkeley
• arXiv: https://arxiv.org/abs/1812.00929

Grid R-CNN

• intro: SenseTime
• arXiv: https://arxiv.org/abs/1811.12030

Deformable ConvNets v2: More Deformable, Better Results

• intro: Microsoft Research Asia

• arXiv: https://arxiv.org/abs/1811.11168

Anchor Box Optimization for Object Detection

• intro: Microsoft Research
• arXiv: https://arxiv.org/abs/1812.00469

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

• intro: https://arxiv.org/abs/1811.12152

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

• arXiv: https://arxiv.org/abs/1812.00124

Learning RoI Transformer for Detecting Oriented Objects in Aerial Images

• arXiv: https://arxiv.org/abs/1812.00155

Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

• intro: Microsoft Research Asia
• arXiv: https://arxiv.org/abs/1811.11167

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

• arXiv: https://arxiv.org/abs/1811.11318

Gradient Harmonized Single-stage Detector

• intro: AAAI 2019
• arXiv: https://arxiv.org/abs/1811.05181

CFENet: Object Detection with Comprehensive Feature Enhancement Module

• intro: ACCV 2018
• github: https://github.com/qijiezhao/CFENet

DeRPN: Taking a further step toward more general object detection

• intro: AAAI 2019
• arXiv: https://arxiv.org/abs/1811.06700
• github: https://github.com/HCIILAB/DeRPN

Hybrid Knowledge Routed Modules for Large-scale Object Detection

• intro: Sun Yat-Sen University & Huawei Noah’s Ark Lab
• arXiv: https://arxiv.org/abs/1810.12681
• github: https://github.com/chanyn/HKRM

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

• intro: ECCV 2018
• arXiv: https://arxiv.org/abs/1711.07767
• github: https://github.com/ruinmessi/RFBNet

Deep Feature Pyramid Reconfiguration for Object Detection

• intro: ECCV 2018
• arXiv: https://arxiv.org/abs/1808.07993

Unsupervised Hard Example Mining from Videos for Improved Object Detection

• intro: ECCV 2018
• arXiv: https://arxiv.org/abs/1808.04285

Acquisition of Localization Confidence for Accurate Object Detection

• intro: ECCV 2018
• arXiv: https://arxiv.org/abs/1807.11590
• github: https://github.com/vacancy/PreciseRoIPooling

Toward Scale-Invariance and Position-Sensitive Region Proposal Networks

• intro: ECCV 2018
• arXiv: https://arxiv.org/abs/1807.09528

MetaAnchor: Learning to Detect Objects with Customized Anchors

• arxiv: https://arxiv.org/abs/1807.00980

Relation Network for Object Detection

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1711.11575
• github:https://github.com/msracver/Relation-Networks-for-Object-Detection

Quantization Mimic: Towards Very Tiny CNN for Object Detection

• Tsinghua University1 & The Chinese University of Hong Kong2 &SenseTime3
• arxiv: https://arxiv.org/abs/1805.02152

Learning Rich Features for Image Manipulation Detection

• intro: CVPR 2018 Camera Ready
• arxiv: https://arxiv.org/abs/1805.04953

SNIPER: Efficient Multi-Scale Training

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

Soft Sampling for Robust Object Detection

• intro: the robustness of object detection under the presence of missing annotations
• arxiv:https://arxiv.org/abs/1806.06986

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

• intro: TNNLS 2018
• arxiv:https://arxiv.org/abs/1807.00147
• code: http://kezewang.com/codes/ASM_ver1.zip

 

Other

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

• arxiv: https://arxiv.org/abs/1808.05560
• youtube: https://youtu.be/xCYD-tYudN0

 

Detection Toolbox

• Detectron(FAIR): Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.

• maskrcnn-benchmark(FAIR): Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

• mmdetection(SenseTime&CUHK): mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.

0002，

deep learning object detection

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

• Paper list from 2014 to now(2019)
• Performance table
• Papers
  • 2014
  • 2015
  • 2016
  • 2017
  • 2018
  • 2019
• Dataset 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] |[pdf] [official code - caffe]

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

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

• [SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | Kaiming He, et al. | [ECCV' 14] |[pdf] [official code - caffe] [unofficial code - keras] [unofficial code - tensorflow]

 

2015

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

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

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

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

• [Fast R-CNN] Fast R-CNN | Ross Girshick | [ICCV' 15] |[pdf] [official code - caffe]

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

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

 

2016

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

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

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

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

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

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

• [CRAPF] CRAFT Objects from Images | Bin Yang, et al. | [CVPR' 16] |[pdf] [official code - caffe]

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

• [SSD] SSD: Single Shot MultiBox Detector | Wei Liu, et al. | [ECCV' 16] |[pdf] [official code - caffe] [unofficial code - tensorflow] [unofficial code - pytorch]

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

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

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

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

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

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

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

 

2017

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

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

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

• [YOLO v2] YOLO9000: Better, Faster, Stronger | Joseph Redmon, Ali Farhadi | [CVPR' 17] |[pdf] [official code - c] [unofficial code - caffe] [unofficial code - tensorflow] [unofficial code - tensorflow] [unofficial code - pytorch]

• [RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | Tao Kong, et al. | [CVPR' 17] |[pdf] [official code - caffe] [unofficial code - tensorflow]

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

• [DCN] Deformable Convolutional Networks | Jifeng Dai, et al. | [ICCV' 17] |[pdf] [official code - mxnet] [unofficial code - tensorflow] [unofficial code - pytorch]

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

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

• [RetinaNet] Focal Loss for Dense Object Detection | Tsung-Yi Lin, et al. | [ICCV' 17] |[pdf] [official code - keras] [unofficial code - pytorch] [unofficial code - mxnet] [unofficial code - tensorflow]

• [Mask R-CNN] Mask R-CNN | Kaiming He, et al. | [ICCV' 17] |[pdf] [official code - caffe2] [unofficial code - tensorflow] [unofficial code - tensorflow] [unofficial code - pytorch]

• [DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | Zhiqiang Shen, et al. | [ICCV' 17] |[pdf] [official code - caffe] [unofficial code - pytorch]

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

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

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

 

2018

• [YOLO v3] YOLOv3: An Incremental Improvement | Joseph Redmon, Ali Farhadi | [arXiv' 18] |[pdf] [official code - c] [unofficial code - pytorch] [unofficial code - pytorch] [unofficial code - keras] [unofficial code - tensorflow]

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

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

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

• [RefineDet] Single-Shot Refinement Neural Network for Object Detection | Shifeng Zhang, et al. | [CVPR' 18] |[pdf] [official code - caffe] [unofficial code - chainer] [unofficial code - pytorch]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

2019

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

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

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

 

Dataset Papers

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

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] | [pdf]

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

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

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

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

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

 

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