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
Imbalance Problems in Object Detection: A Review
- intro: under review at TPAMI
- arXiv: https://arxiv.org/abs/1909.00169
Recent Advances in Deep Learning for Object Detection
- intro: From 2013 (OverFeat) to 2019 (DetNAS)
- arXiv: https://arxiv.org/abs/1908.03673
A Survey of Deep Learning-based Object Detection
-
intro：From Fast R-CNN to NAS-FPN
- arXiv：https://arxiv.org/abs/1907.09408
Object Detection in 20 Years: A Survey
- intro：This work has been submitted to the IEEE TPAMI for possible publication
- arXiv：https://arxiv.org/abs/1905.05055
《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 Object Detection）
Zero-Shot Detection
- intro: Australian National University
- keywords: YOLO
- arxiv: https://arxiv.org/abs/1803.07113
Zero-Shot Object Detection
- arxiv: https://arxiv.org/abs/1804.04340
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
- 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）
Comparison Network for One-Shot Conditional Object Detection
- arXiv: https://arxiv.org/abs/1904.02317
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
Feature Selective Anchor-Free Module for Single-Shot Object Detection
-
intro: CVPR 2019
- arXiv: https://arxiv.org/abs/1903.00621
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.
- Detectron2: Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
- 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.