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
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[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.
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[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]
Contact & Feedback
If you have any suggestions about papers, feel free to mail me :)
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