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
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
Methods
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
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
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
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
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
Few-shot Object Detection
Yes-Net: An effective Detector Based on Global Information
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
Towards lightweight convolutional neural networks for object detection
RON: Reverse Connection with Objectness Prior Networks for Object Detection
- intro: CVPR 2017
- arxiv: 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
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: https://github.com/rbgirshick/py-faster-rcnn
- github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn
- github: https://github.com//jwyang/faster-rcnn.pytorch
- github: https://github.com/mitmul/chainer-faster-rcnn
- github: https://github.com/andreaskoepf/faster-rcnn.torch
- github: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
- github: https://github.com/smallcorgi/Faster-RCNN_TF
- github: https://github.com/CharlesShang/TFFRCNN
- github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
- github: https://github.com/yhenon/keras-frcnn
- github: https://github.com/Eniac-Xie/faster-rcnn-resnet
- github(C++): https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev
R-CNN minus R
- intro: BMVC 2015
- arxiv: http://arxiv.org/abs/1506.06981
Faster R-CNN in MXNet with distributed implementation and data parallelization
Contextual Priming and Feedback for Faster R-CNN
- intro: ECCV 2016. Carnegie Mellon University
- paper: http://abhinavsh.info/context_priming_feedback.pdf
- poster: http://www.eccv2016.org/files/posters/P-1A-20.pdf
An Implementation of Faster RCNN with Study for Region Sampling
- intro: Technical Report, 3 pages. CMU
- arxiv: https://arxiv.org/abs/1702.02138
- github: https://github.com/endernewton/tf-faster-rcnn
Interpretable R-CNN
- intro: North Carolina State University & Alibaba
- keywords: AND-OR Graph (AOG)
- arxiv: https://arxiv.org/abs/1711.05226
Light-Head R-CNN: In Defense of Two-Stage Object Detector
- intro: Tsinghua University & Megvii Inc
- arxiv: https://arxiv.org/abs/1711.07264
- github(official, Tensorflow): https://github.com/zengarden/light_head_rcnn
- github: https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet_v1_101_rfcn_light.py#L784
Cascade R-CNN: Delving into High Quality Object Detection
- intro: CVPR 2018. UC San Diego
- arxiv: https://arxiv.org/abs/1712.00726
- github(Caffe, official): https://github.com/zhaoweicai/cascade-rcnn
Cascade R-CNN: High Quality Object Detection and Instance Segmentation
-arxiv: https://arxiv.org/abs/1906.09756
- github(Caffe, official): https://github.com/zhaoweicai/cascade-rcnn
- github(official): https://github.com/zhaoweicai/Detectron-Cascade-RCNN
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution
- intro: NeurIPS 2019 spotlight
- arxiv: https://arxiv.org/abs/1909.06720
- github: https://github.com/thangvubk/Cascade-RPN
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
https://arxiv.org/abs/1706.10217
Domain Adaptive Faster R-CNN for Object Detection in the Wild
- intro: CVPR 2018. ETH Zurich & ESAT/PSI
- arxiv: https://arxiv.org/abs/1803.03243
- github(official. Caffe): https://github.com/yuhuayc/da-faster-rcnn
Robust Physical Adversarial Attack on Faster R-CNN Object Detector
https://arxiv.org/abs/1804.05810
Auto-Context R-CNN
- intro: Rejected by ECCV18
- arxiv: https://arxiv.org/abs/1807.02842
Grid R-CNN
- intro: CVPR 2019
- intro: SenseTime
- arxiv: https://arxiv.org/abs/1811.12030
Grid R-CNN Plus: Faster and Better
- intro: SenseTime Research & CUHK & Beihang University
- arxiv: https://arxiv.org/abs/1906.05688
- github: https://github.com/STVIR/Grid-R-CNN
Few-shot Adaptive Faster R-CNN
- intro: CVPR 2019
- arxiv: https://arxiv.org/abs/1903.09372
Libra R-CNN: Towards Balanced Learning for Object Detection
- intro: CVPR 2019
- arxiv: https://arxiv.org/abs/1904.02701
Rethinking Classification and Localization in R-CNN
- intro: Northeastern University & Microsoft
- arxiv: https://arxiv.org/abs/1904.06493
Reprojection R-CNN: A Fast and Accurate Object Detector for 360° Images
- intro: Peking University
- arxiv: https://arxiv.org/abs/1907.11830
Rethinking Classification and Localization for Cascade R-CNN
- intro: BMVC 2019
- arxiv: https://arxiv.org/abs/1907.11914
IoU-uniform R-CNN: Breaking Through the Limitations of RPN
- arxiv: https://arxiv.org/abs/1912.05190
- github(mmdetection): https://github.com/zl1994/IoU-Uniform-R-CNN
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
Delving into the Imbalance of Positive Proposals in Two-stage Object Detection
- intro: Waseda University & Tencent AI Lab & Nanjing University of Science and Technology
- arxiv: https://arxiv.org/abs/2005.11472
Mask R-CNN
-
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
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: http://pjreddie.com/darknet/yolo/
- github: https://github.com/pjreddie/darknet
- blog: https://pjreddie.com/publications/yolo/
- slides: https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p
- github: https://github.com/gliese581gg/YOLO_tensorflow
- github: https://github.com/xingwangsfu/caffe-yolo
- github: https://github.com/frankzhangrui/Darknet-Yolo
- github: https://github.com/BriSkyHekun/py-darknet-yolo
- github: https://github.com/tommy-qichang/yolo.torch
- github: https://github.com/frischzenger/yolo-windows
- github: https://github.com/AlexeyAB/yolo-windows
- github: https://github.com/nilboy/tensorflow-yolo
darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
- blog: https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp
- github: https://github.com/thtrieu/darkflow
Start Training YOLO with Our Own Data
- intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
- blog: http://guanghan.info/blog/en/my-works/train-yolo/
- github: https://github.com/Guanghan/darknet
YOLO: Core ML versus MPSNNGraph
- intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
- blog: http://machinethink.net/blog/yolo-coreml-versus-mps-graph/
- github: https://github.com/hollance/YOLO-CoreML-MPSNNGraph
TensorFlow YOLO object detection on Android
- intro: Real-time object detection on Android using the YOLO network with TensorFlow
- github: https://github.com/natanielruiz/android-yolo
Computer Vision in iOS – Object Detection
- blog: https://sriraghu.com/2017/07/12/computer-vision-in-ios-object-detection/
- github:https://github.com/r4ghu/iOS-CoreML-Yolo
YOLOv2
YOLO9000: Better, Faster, Stronger
- arxiv: https://arxiv.org/abs/1612.08242
- code: http://pjreddie.com/yolo9000/
- github(Chainer): https://github.com/leetenki/YOLOv2
- github(Keras): https://github.com/allanzelener/YAD2K
- github(PyTorch): https://github.com/longcw/yolo2-pytorch
- github(Tensorflow): https://github.com/hizhangp/yolo_tensorflow
- github(Windows): https://github.com/AlexeyAB/darknet
- github: https://github.com/choasUp/caffe-yolo9000
- github: https://github.com/philipperemy/yolo-9000
darknet_scripts
- intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
- github: https://github.com/Jumabek/darknet_scripts
Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
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
YOLOv3
YOLOv3: An Incremental Improvement
- project page: https://pjreddie.com/darknet/yolo/
- paper: https://pjreddie.com/media/files/papers/YOLOv3.pdf
- arxiv: https://arxiv.org/abs/1804.02767
- githb: https://github.com/DeNA/PyTorch_YOLOv3
- github: https://github.com/eriklindernoren/PyTorch-YOLOv3
Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving
https://arxiv.org/abs/1904.04620
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
https://arxiv.org/abs/1811.05588
Spiking-YOLO: Spiking Neural Network for Real-time Object Detection
https://arxiv.org/abs/1903.06530
YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
https://arxiv.org/abs/1910.01271
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs
https://arxiv.org/abs/1909.13396
Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3
- intro: TPAMI
- arxiv: https://arxiv.org/abs/2005.13243
- gitlab: https://gitlab.com/irafm-ai/poly-yolo
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
YOLOv4
YOLOv4: Optimal Speed and Accuracy of Object Detection
- keywords: Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT), Mish-activation
- arxiv: https://arxiv.org/abs/2004.10934
- github: https://github.com/AlexeyAB/darknet
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 : 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: 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
Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
- keywords: ESSD
- arxiv: 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: Multi-scale Deconvolutional Single Shot Detector for small objects
- intro: Zhengzhou University
- arxiv: https://arxiv.org/abs/1805.07009
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
Residual Features and Unified Prediction Network for Single Stage Detection
https://arxiv.org/abs/1707.05031
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
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
Feature Selective Networks for Object Detection
https://arxiv.org/abs/1711.08879
Learning a Rotation Invariant Detector with Rotatable Bounding Box
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
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
Object Detection with Mask-based Feature Encoding
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
Transferring Common-Sense Knowledge for Object Detection
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
Issues
Anchor-free
Feature Selective Anchor-Free Module for Single-Shot Object Detection
- intro: CVPR 2019
- arxiv: https://arxiv.org/abs/1903.00621
FCOS: Fully Convolutional One-Stage Object Detection
- intro: The University of Adelaide
- keywords: anchor-free
- arxiv: https://arxiv.org/abs/1904.01355
- github: https://github.com/tianzhi0549/FCOS/
FoveaBox: Beyond Anchor-based Object Detector
- intro: Tsinghua University & BNRist & ByteDance AI Lab & University of Pennsylvania
- arxiv: https://arxiv.org/abs/1904.03797
- github(official, mmdetection): https://github.com/taokong/FoveaBox
IMMVP: An Efficient Daytime and Nighttime On-Road Object Detector
https://arxiv.org/abs/1910.06573
EfficientDet: Scalable and Efficient Object Detection
- intro: CVPR 2020
- arxiv: https://arxiv.org/abs/1911.09070
- github: https://github.com/google/automl/tree/master/efficientdet
- github: https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch
Domain Adaptation for Object Detection via Style Consistency
- intro: BMVC 2019
- arxiv: https://arxiv.org/abs/1911.10033
Soft Anchor-Point Object Detection
- intro: Carnegie Mellon University
- keywords: Soft Anchor-Point Detector (SAPD)
- arxiv: https://arxiv.org/abs/1911.12448
IPG-Net: Image Pyramid Guidance Network for Object Detection
https://arxiv.org/abs/1912.00632
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
Localization Uncertainty Estimation for Anchor-Free Object Detection
- keywords: Gaussian-FCOS
- arxiv: https://arxiv.org/abs/2006.15607
Dive Deeper Into Box for Object Detection
- intro: ECCV 2020
- keywords: DDBNet, anchor free
- arxiv: https://arxiv.org/abs/2007.14350
Corner Proposal Network for Anchor-free, Two-stage Object Detection
- intro: ECCV 2020
- arxiv: https://arxiv.org/abs/2007.13816
- github: https://github.com/Duankaiwen/CPNDet
Non-Maximum Suppression (NMS)
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
- intro: CVPR 2015
- arxiv: http://arxiv.org/abs/1411.5309
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wan_End-to-End_Integration_of_2015_CVPR_paper.pdf
A convnet for non-maximum suppression
Improving Object Detection With One Line of Code
Soft-NMS – Improving Object Detection With One Line of Code
- intro: ICCV 2017. University of Maryland
- keywords: Soft-NMS
- arxiv: https://arxiv.org/abs/1704.04503
- github: https://github.com/bharatsingh430/soft-nms
Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection
- intro: CMU & Megvii Inc. (Face++)
- arxiv: https://arxiv.org/abs/1809.08545
- github: https://github.com/yihui-he/softer-NMS
Learning non-maximum suppression
- intro: CVPR 2017
- project page: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/learning-nms/
- arxiv: https://arxiv.org/abs/1705.02950
- github: https://github.com/hosang/gossipnet
Relation Networks for Object Detection
- intro: CVPR 2018 oral
- arxiv: https://arxiv.org/abs/1711.11575
- github(official, MXNet): https://github.com/msracver/Relation-Networks-for-Object-Detection
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
- keywords: Pairwise-NMS
- arxiv: https://arxiv.org/abs/1901.03796
Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
https://arxiv.org/abs/1902.02067
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing
- intro: CVPR 2020
- intro: Waseda University & Tencent AI Lab
- arxiv: https://arxiv.org/abs/2003.12729
Hashing-based Non-Maximum Suppression for Crowded Object Detection
- intro: Microsoft
- arxiv: https://arxiv.org/abs/2005.11426
- github: https://github.com/microsoft/hnms
Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes
- intro: NeurIPS 2019, Machine Learning for Autonomous Driving Workshop
- intro: Mercedes-Benz AG, R&D & University of Jena
- keywords: Visibility Guided NMS (vg-NMS)
- arxiv: https://arxiv.org/abs/2006.08547
Adversarial Examples
Adversarial Examples that Fool Detectors
- intro: University of Illinois
- arxiv: https://arxiv.org/abs/1712.02494
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
- project page: http://nicholas.carlini.com/code/nn_breaking_detection/
- arxiv: https://arxiv.org/abs/1705.07263
- github: https://github.com/carlini/nn_breaking_detection
Mimic / Knowledge Distillation
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
Quantization Mimic: Towards Very Tiny CNN for Object Detection
- intro: ECCV 2018
- arxiv: https://arxiv.org/abs/1805.02152
Learning Efficient Detector with Semi-supervised Adaptive Distillation
- intro: SenseTime Research
- arxiv: https://arxiv.org/abs/1901.00366
- github: https://github.com/Tangshitao/Semi-supervised-Adaptive-Distillation
Distilling Object Detectors with Fine-grained Feature Imitation
- intro: CVPR 2019
- intro: National University of Singapore & Huawei Noah’s Ark Lab
- keywords: mimic
- arxiv: https://arxiv.org/abs/1906.03609
- github: https://github.com/twangnh/Distilling-Object-Detectors
GAN-Knowledge Distillation for one-stage Object Detection
https://arxiv.org/abs/1906.08467
Learning Lightweight Pedestrian Detector with Hierarchical Knowledge Distillation
- intro: ICIP 2019 oral
- arxiv: https://arxiv.org/abs/1909.09325
Special Object Detection
Weakly Supervised Object Detection
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
- intro: CVPR 2016
- arxiv: http://arxiv.org/abs/1604.05766
Weakly supervised object detection using pseudo-strong labels
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
- intro: IJCAI 2017
- arxiv: https://arxiv.org/abs/1706.06768
Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
- intro: TPAMI 2017. National Institutes of Health (NIH) Clinical Center
- arxiv: https://arxiv.org/abs/1801.03145
Video Object Detection
Learning Object Class Detectors from Weakly Annotated Video
- intro: CVPR 2012
- paper: https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_00905.pdf
Analysing domain shift factors between videos and images for object detection
Video Object Recognition
Deep Learning for Saliency Prediction in Natural Video
- intro: Submitted on 12 Jan 2016
- keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
- paper: https://hal.archives-ouvertes.fr/hal-01251614/document
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
- intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
- arxiv: http://arxiv.org/abs/1604.02532
- github: https://github.com/myfavouritekk/T-CNN
Object Detection from Video Tubelets with Convolutional Neural Networks
- intro: CVPR 2016 Spotlight paper
- arxiv: https://arxiv.org/abs/1604.04053
- paper: http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf
- gihtub: https://github.com/myfavouritekk/vdetlib
Object Detection in Videos with Tubelets and Multi-context Cues
- intro: SenseTime Group
- slides: http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf
- slides: http://image-net.org/challenges/talks/Object%20Detection%20in%20Videos%20with%20Tubelets%20and%20Multi-context%20Cues%20-%20Final.pdf
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
- intro: BMVC 2016
- keywords: pseudo-labeler
- arxiv: http://arxiv.org/abs/1607.04648
- paper: http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf
CNN Based Object Detection in Large Video Images
- intro: WangTao @ 爱奇艺
- keywords: object retrieval, object detection, scene classification
- slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6362-wang-tao-cnn-based-object-detection-large-video-images.pdf
Object Detection in Videos with Tubelet Proposal Networks
Flow-Guided Feature Aggregation for Video Object Detection
- intro: MSRA
- arxiv: https://arxiv.org/abs/1703.10025
Video Object Detection using Faster R-CNN
- blog: http://andrewliao11.github.io/object_detection/faster_rcnn/
- github: https://github.com/andrewliao11/py-faster-rcnn-imagenet
Improving Context Modeling for Video Object Detection and Tracking
http://image-net.org/challenges/talks_2017/ilsvrc2017_short(poster).pdf
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1708.00666
Mobile Video Object Detection with Temporally-Aware Feature Maps
https://arxiv.org/abs/1711.06368
Towards High Performance Video Object Detection
https://arxiv.org/abs/1711.11577
Impression Network for Video Object Detection
https://arxiv.org/abs/1712.05896
Spatial-Temporal Memory Networks for Video Object Detection
https://arxiv.org/abs/1712.06317
3D-DETNet: a Single Stage Video-Based Vehicle Detector
https://arxiv.org/abs/1801.01769
Object Detection in Videos by Short and Long Range Object Linking
https://arxiv.org/abs/1801.09823
Object Detection in Video with Spatiotemporal Sampling Networks
- intro: University of Pennsylvania, 2Dartmouth College
- arxiv: https://arxiv.org/abs/1803.05549
Towards High Performance Video Object Detection for Mobiles
- intro: Microsoft Research Asia
- arxiv: https://arxiv.org/abs/1804.05830
Optimizing Video Object Detection via a Scale-Time Lattice
- intro: CVPR 2018
- project page: http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice/
- arxiv: https://arxiv.org/abs/1804.05472
- github: https://github.com/hellock/scale-time-lattice
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing
https://arxiv.org/abs/1809.01701
Fast Object Detection in Compressed Video
https://arxiv.org/abs/1811.11057
Tube-CNN: Modeling temporal evolution of appearance for object detection in video
- intro: INRIA/ENS
- arxiv: https://arxiv.org/abs/1812.02619
AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
- intro: SysML 2019 oral
- arxiv: https://arxiv.org/abs/1902.02910
SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection
- intro: AAAI 2019
- arxiv: https://arxiv.org/abs/1903.07663
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
- intro: Cornell University & Google AI
- arxiv: https://arxiv.org/abs/1903.10172
Progressive Sparse Local Attention for Video object detection
- intro: NLPR,CASIA & Horizon Robotics
- arxiv: https://arxiv.org/abs/1903.09126
Sequence Level Semantics Aggregation for Video Object Detection
https://arxiv.org/abs/1907.06390
Object Detection in Video with Spatial-temporal Context Aggregation
- intro: Huazhong University of Science and Technology & Horizon Robotics Inc.
- arxiv: https://arxiv.org/abs/1907.04988
A Delay Metric for Video Object Detection: What Average Precision Fails to Tell
- intro: ICCV 2019
- arxiv: https://arxiv.org/abs/1908.06368
Minimum Delay Object Detection From Video
- intro: ICCV 2019
- arxiv: https://arxiv.org/abs/1908.11092
Learning Motion Priors for Efficient Video Object Detection
https://arxiv.org/abs/1911.05253
Object Detection on Mobile Devices
Pelee: A Real-Time Object Detection System on Mobile Devices
- intro: ICLR 2018 workshop track
- intro: based on the SSD
- arxiv: https://arxiv.org/abs/1804.06882
- github: https://github.com/Robert-JunWang/Pelee
Object Detection on RGB-D
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Differential Geometry Boosts Convolutional Neural Networks for Object Detection
- intro: CVPR 2016
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w23/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html
A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
https://arxiv.org/abs/1703.03347
Cross-Modal Attentional Context Learning for RGB-D Object Detection
- intro: IEEE Transactions on Image Processing
- arxiv: https://arxiv.org/abs/1810.12829
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
Zero-Shot Object Detection (ZSD)
Zero-Shot Detection
- intro: Australian National University
- keywords: YOLO
- arxiv: https://arxiv.org/abs/1803.07113
Zero-Shot Object Detection
https://arxiv.org/abs/1804.04340
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
- intro: Australian National University
- arxiv: https://arxiv.org/abs/1803.06049
Zero-Shot Object Detection by Hybrid Region Embedding
- intro: Middle East Technical University & Hacettepe University
- arxiv: https://arxiv.org/abs/1805.06157
One-Shot Object Detection (OSD)
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
Visual Relationship Detection
Visual Relationship Detection with Language Priors
- intro: ECCV 2016 oral
- paper: https://cs.stanford.edu/people/ranjaykrishna/vrd/vrd.pdf
- github: https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection
ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection
- intro: Visual Phrase reasoning Convolutional Neural Network (ViP-CNN), Visual Phrase Reasoning Structure (VPRS)
- arxiv: https://arxiv.org/abs/1702.07191
Visual Translation Embedding Network for Visual Relation Detection
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection
- intro: CVPR 2017 spotlight paper
- arxiv: https://arxiv.org/abs/1703.03054
Detecting Visual Relationships with Deep Relational Networks
- intro: CVPR 2017 oral. The Chinese University of Hong Kong
- arxiv: https://arxiv.org/abs/1704.03114
Identifying Spatial Relations in Images using Convolutional Neural Networks
https://arxiv.org/abs/1706.04215
PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN
- intro: ICCV
- arxiv: https://arxiv.org/abs/1708.01956
Natural Language Guided Visual Relationship Detection
https://arxiv.org/abs/1711.06032
Detecting Visual Relationships Using Box Attention
- intro: Google AI & IST Austria
- arxiv: https://arxiv.org/abs/1807.02136
Google AI Open Images - Visual Relationship Track
- intro: Detect pairs of objects in particular relationships
- kaggle: https://www.kaggle.com/c/google-ai-open-images-visual-relationship-track
Context-Dependent Diffusion Network for Visual Relationship Detection
- intro: 2018 ACM Multimedia Conference
- arxiv: https://arxiv.org/abs/1809.06213
A Problem Reduction Approach for Visual Relationships Detection
- intro: ECCV 2018 Workshop
- arxiv: https://arxiv.org/abs/1809.09828
Exploring the Semantics for Visual Relationship Detection
https://arxiv.org/abs/1904.02104
Face Detection
Multi-view Face Detection Using Deep Convolutional Neural Networks
- intro: Yahoo
- arxiv: http://arxiv.org/abs/1502.02766
- github: https://github.com/guoyilin/FaceDetection_CNN
From Facial Parts Responses to Face Detection: A Deep Learning Approach
- intro: ICCV 2015. CUHK
- project page: http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html
- arxiv: https://arxiv.org/abs/1509.06451
- paper: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yang_From_Facial_Parts_ICCV_2015_paper.pdf
Compact Convolutional Neural Network Cascade for Face Detection
- arxiv: http://arxiv.org/abs/1508.01292
- github: https://github.com/Bkmz21/FD-Evaluation
- github: https://github.com/Bkmz21/CompactCNNCascade
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
- intro: ECCV 2016
- arxiv: https://arxiv.org/abs/1606.00850
- github(MXNet): https://github.com/tfwu/FaceDetection-ConvNet-3D
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
- intro: CMU
- arxiv: https://arxiv.org/abs/1606.05413
Towards a Deep Learning Framework for Unconstrained Face Detection
- intro: overlap with CMS-RCNN
- arxiv: https://arxiv.org/abs/1612.05322
Supervised Transformer Network for Efficient Face Detection
UnitBox: An Advanced Object Detection Network
- intro: ACM MM 2016
- keywords: IOULoss
- arxiv: http://arxiv.org/abs/1608.01471
Bootstrapping Face Detection with Hard Negative Examples
- author: 万韶华 @ 小米.
- intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset
- arxiv: http://arxiv.org/abs/1608.02236
Grid Loss: Detecting Occluded Faces
- intro: ECCV 2016
- arxiv: https://arxiv.org/abs/1609.00129
- paper: http://lrs.icg.tugraz.at/pubs/opitz_eccv_16.pdf
- poster: http://www.eccv2016.org/files/posters/P-2A-34.pdf
A Multi-Scale Cascade Fully Convolutional Network Face Detector
- intro: ICPR 2016
- arxiv: http://arxiv.org/abs/1609.03536
MTCNN
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks
- project page: https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html
- arxiv: https://arxiv.org/abs/1604.02878
- github(official, Matlab): https://github.com/kpzhang93/MTCNN_face_detection_alignment
- github: https://github.com/pangyupo/mxnet_mtcnn_face_detection
- github: https://github.com/DaFuCoding/MTCNN_Caffe
- github(MXNet): https://github.com/Seanlinx/mtcnn
- github: https://github.com/Pi-DeepLearning/RaspberryPi-FaceDetection-MTCNN-Caffe-With-Motion
- github(Caffe): https://github.com/foreverYoungGitHub/MTCNN
- github: https://github.com/CongWeilin/mtcnn-caffe
- github(OpenCV+OpenBlas): https://github.com/AlphaQi/MTCNN-light
- github(Tensorflow+golang): https://github.com/jdeng/goface
Face Detection using Deep Learning: An Improved Faster RCNN Approach
- intro: DeepIR Inc
- arxiv: https://arxiv.org/abs/1701.08289
Faceness-Net: Face Detection through Deep Facial Part Responses
- intro: An extended version of ICCV 2015 paper
- arxiv: https://arxiv.org/abs/1701.08393
Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”
- intro: CVPR 2017. MP-RCNN, MP-RPN
- arxiv: https://arxiv.org/abs/1703.09145
End-To-End Face Detection and Recognition
https://arxiv.org/abs/1703.10818
Face R-CNN
https://arxiv.org/abs/1706.01061
Face Detection through Scale-Friendly Deep Convolutional Networks
https://arxiv.org/abs/1706.02863
Scale-Aware Face Detection
- intro: CVPR 2017. SenseTime & Tsinghua University
- arxiv: https://arxiv.org/abs/1706.09876
Detecting Faces Using Inside Cascaded Contextual CNN
- intro: CVPR 2017. Tencent AI Lab & SenseTime
- paper: http://ai.tencent.com/ailab/media/publications/Detecting_Faces_Using_Inside_Cascaded_Contextual_CNN.pdf
Multi-Branch Fully Convolutional Network for Face Detection
https://arxiv.org/abs/1707.06330
SSH: Single Stage Headless Face Detector
- intro: ICCV 2017. University of Maryland
- arxiv: https://arxiv.org/abs/1708.03979
- github(official, Caffe): https://github.com/mahyarnajibi/SSH
Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container
https://arxiv.org/abs/1708.04370
FaceBoxes: A CPU Real-time Face Detector with High Accuracy
- intro: IJCB 2017
- keywords: Rapidly Digested Convolutional Layers (RDCL), Multiple Scale Convolutional Layers (MSCL)
- intro: the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images
- arxiv: https://arxiv.org/abs/1708.05234
- github(official): https://github.com/sfzhang15/FaceBoxes
- github(Caffe): https://github.com/zeusees/FaceBoxes
S3FD: Single Shot Scale-invariant Face Detector
- intro: ICCV 2017. Chinese Academy of Sciences
- intro: can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images
- arxiv: https://arxiv.org/abs/1708.05237
- github(Caffe, official): https://github.com/sfzhang15/SFD
- github: https://github.com//clcarwin/SFD_pytorch
Detecting Faces Using Region-based Fully Convolutional Networks
https://arxiv.org/abs/1709.05256
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
https://arxiv.org/abs/1709.07326
Face Attention Network: An effective Face Detector for the Occluded Faces
https://arxiv.org/abs/1711.07246
Feature Agglomeration Networks for Single Stage Face Detection
https://arxiv.org/abs/1712.00721
Face Detection Using Improved Faster RCNN
- intro: Huawei Cloud BU
- arxiv: https://arxiv.org/abs/1802.02142
PyramidBox: A Context-assisted Single Shot Face Detector
- intro: Baidu, Inc
- arxiv: https://arxiv.org/abs/1803.07737
PyramidBox++: High Performance Detector for Finding Tiny Face
- intro: Chinese Academy of Sciences & Baidu, Inc.
- arxiv: https://arxiv.org/abs/1904.00386
A Fast Face Detection Method via Convolutional Neural Network
- intro: Neurocomputing
- arxiv: https://arxiv.org/abs/1803.10103
Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
- intro: CVPR 2018. Beihang University & CUHK & Sensetime
- arxiv: https://arxiv.org/abs/1804.05197
Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1804.06039
- github(binary library): https://github.com/Jack-CV/PCN
SFace: An Efficient Network for Face Detection in Large Scale Variations
- intro: Beihang University & Megvii Inc. (Face++)
- arxiv: https://arxiv.org/abs/1804.06559
Survey of Face Detection on Low-quality Images
https://arxiv.org/abs/1804.07362
Anchor Cascade for Efficient Face Detection
- intro: The University of Sydney
- arxiv: https://arxiv.org/abs/1805.03363
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization
- intro: IEEE MMSP
- arxiv: https://arxiv.org/abs/1805.12302
Selective Refinement Network for High Performance Face Detection
https://arxiv.org/abs/1809.02693
DSFD: Dual Shot Face Detector
https://arxiv.org/abs/1810.10220
Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision
https://arxiv.org/abs/1811.08557
FA-RPN: Floating Region Proposals for Face Detection
https://arxiv.org/abs/1812.05586
Robust and High Performance Face Detector
https://arxiv.org/abs/1901.02350
DAFE-FD: Density Aware Feature Enrichment for Face Detection
https://arxiv.org/abs/1901.05375
Improved Selective Refinement Network for Face Detection
- intro: Chinese Academy of Sciences & JD AI Research
- arxiv: https://arxiv.org/abs/1901.06651
Revisiting a single-stage method for face detection
https://arxiv.org/abs/1902.01559
MSFD:Multi-Scale Receptive Field Face Detector
- intro: ICPR 2018
- arxiv: https://arxiv.org/abs/1903.04147
LFFD: A Light and Fast Face Detector for Edge Devices
- arxiv: https://arxiv.org/abs/1904.10633
- github: https://github.com/YonghaoHe/A-Light-and-Fast-Face-Detector-for-Edge-Devices
RetinaFace: Single-stage Dense Face Localisation in the Wild
- arxiv: https://arxiv.org/abs/1905.00641
- gihtub: https://github.com/deepinsight/insightface/tree/master/RetinaFace
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
- intro: CVPR Workshop on Computer Vision for Augmented and Virtual Reality, 2019
- arxiv: https://arxiv.org/abs/1907.05047
HAMBox: Delving into Online High-quality Anchors Mining for Detecting Outer Faces
- intro: Baidu Inc. & Chinese Academy of Sciences
- arxiv: https://arxiv.org/abs/1912.09231
KPNet: Towards Minimal Face Detector
- intro: AAAI 2020
- arxiv: https://arxiv.org/abs/2003.07543
ASFD: Automatic and Scalable Face Detector
- intro: Youtu Lab, Tencent & Southeast University & Xiamen University
- arxiv: https://arxiv.org/abs/2003.11228
Detect Small Faces
Finding Tiny Faces
- intro: CVPR 2017. CMU
- project page: http://www.cs.cmu.edu/~peiyunh/tiny/index.html
- arxiv: https://arxiv.org/abs/1612.04402
- github(official, Matlab): https://github.com/peiyunh/tiny
- github(inference-only): https://github.com/chinakook/hr101_mxnet
- github: https://github.com/cydonia999/Tiny_Faces_in_Tensorflow
Detecting and counting tiny faces
- intro: ENS Paris-Saclay. ExtendedTinyFaces
- intro: Detecting and counting small objects - Analysis, review and application to counting
- arxiv: https://arxiv.org/abs/1801.06504
- github: https://github.com/alexattia/ExtendedTinyFaces
Seeing Small Faces from Robust Anchor’s Perspective
- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1802.09058
Face-MagNet: Magnifying Feature Maps to Detect Small Faces
- intro: WACV 2018
- keywords: Face Magnifier Network (Face-MageNet)
- arxiv: https://arxiv.org/abs/1803.05258
- github: https://github.com/po0ya/face-magnet
Robust Face Detection via Learning Small Faces on Hard Images
- intro: Johns Hopkins University & Stanford University
- arxiv: https://arxiv.org/abs/1811.11662
- github: https://github.com/bairdzhang/smallhardface
SFA: Small Faces Attention Face Detector
- intro: Jilin University
- arxiv: https://arxiv.org/abs/1812.08402
Person Head Detection
Context-aware CNNs for person head detection
- intro: ICCV 2015
- project page: http://www.di.ens.fr/willow/research/headdetection/
- arxiv: http://arxiv.org/abs/1511.07917
- github: https://github.com/aosokin/cnn_head_detection
Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture
https://arxiv.org/abs/1803.09256
A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications
https://arxiv.org/abs/1809.03336
FCHD: A fast and accurate head detector
- arxiv: https://arxiv.org/abs/1809.08766
- github(PyTorch, official): https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector
Relational Learning for Joint Head and Human Detection
- keywords: JointDet, head-body Relationship Discriminating Module (RDM)
- arxiv: https://arxiv.org/abs/1909.10674
Pedestrian Detection / People Detection
Pedestrian Detection aided by Deep Learning Semantic Tasks
- intro: CVPR 2015
- project page: http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/
- arxiv: http://arxiv.org/abs/1412.0069
Deep Learning Strong Parts for Pedestrian Detection
- intro: ICCV 2015. CUHK. DeepParts
- intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset
- paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/tianLWTiccv15.pdf
Taking a Deeper Look at Pedestrians
- intro: CVPR 2015
- arxiv: https://arxiv.org/abs/1501.05790
Convolutional Channel Features
- intro: ICCV 2015
- arxiv: https://arxiv.org/abs/1504.07339
- github: https://github.com/byangderek/CCF
End-to-end people detection in crowded scenes
- arxiv: http://arxiv.org/abs/1506.04878
- github: https://github.com/Russell91/reinspect
- ipn: http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation_reinspect.ipynb
- youtube: https://www.youtube.com/watch?v=QeWl0h3kQ24
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
- intro: ICCV 2015
- arxiv: https://arxiv.org/abs/1507.05348
Deep convolutional neural networks for pedestrian detection
Scale-aware Fast R-CNN for Pedestrian Detection
New algorithm improves speed and accuracy of pedestrian detection
Pushing the Limits of Deep CNNs for Pedestrian Detection
- intro: “set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from 11.7% to 8.9%”
- arxiv: http://arxiv.org/abs/1603.04525
A Real-Time Deep Learning Pedestrian Detector for Robot Navigation
A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation
Is Faster R-CNN Doing Well for Pedestrian Detection?
- intro: ECCV 2016
- arxiv: http://arxiv.org/abs/1607.07032
- github: https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian
Unsupervised Deep Domain Adaptation for Pedestrian Detection
- intro: ECCV Workshop 2016
- arxiv: https://arxiv.org/abs/1802.03269
Reduced Memory Region Based Deep Convolutional Neural Network Detection
- intro: IEEE 2016 ICCE-Berlin
- arxiv: http://arxiv.org/abs/1609.02500
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
Detecting People in Artwork with CNNs
- intro: ECCV 2016 Workshops
- arxiv: https://arxiv.org/abs/1610.08871
Deep Multi-camera People Detection
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
- intro: CVPR 2017
- project page: http://ml.cs.tsinghua.edu.cn:5000/publications/synunity/
- arxiv: https://arxiv.org/abs/1703.06283
- github(Tensorflow): https://github.com/huangshiyu13/RPNplus
What Can Help Pedestrian Detection?
- intro: CVPR 2017. Tsinghua University & Peking University & Megvii Inc.
- keywords: Faster R-CNN, HyperLearner
- arxiv: https://arxiv.org/abs/1705.02757
- paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Mao_What_Can_Help_CVPR_2017_paper.pdf
Illuminating Pedestrians via Simultaneous Detection & Segmentation
[https://arxiv.org/abs/1706.08564](https://arxiv.org/abs/1706.08564
Rotational Rectification Network for Robust Pedestrian Detection
- intro: CMU & Volvo Construction
- arxiv: https://arxiv.org/abs/1706.08917
STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos
- intro: The University of North Carolina at Chapel Hill
- arxiv: https://arxiv.org/abs/1707.09100
Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy
https://arxiv.org/abs/1709.00235
Aggregated Channels Network for Real-Time Pedestrian Detection
https://arxiv.org/abs/1801.00476
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection
https://arxiv.org/abs/1804.00872
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
https://arxiv.org/abs/1804.02047
PCN: Part and Context Information for Pedestrian Detection with CNNs
- intro: British Machine Vision Conference(BMVC) 2017
- arxiv: https://arxiv.org/abs/1804.04483
Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors
- intro: CVPR 2018
- paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Noh_Improving_Occlusion_and_CVPR_2018_paper.pdf
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
- intro: ECCV 2018
- intro: Hikvision Research Institute
- arxiv: https://arxiv.org/abs/1807.01438
Bi-box Regression for Pedestrian Detection and Occlusion Estimation
- intro: ECCV 2018
- paper: http://openaccess.thecvf.com/content_ECCV_2018/papers/CHUNLUAN_ZHOU_Bi-box_Regression_for_ECCV_2018_paper.pdf
- github(Pytorch): https://github.com/rainofmine/Bi-box_Regression
Pedestrian Detection with Autoregressive Network Phases
- intro: Michigan State University
- arxiv: https://arxiv.org/abs/1812.00440
SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection
https://arxiv.org/abs/1902.09080
High-level Semantic Feature Detection:A New Perspective for Pedestrian Detection
Center and Scale Prediction: A Box-free Approach for Object Detection
- intro: CVPR 2019
- intro: National University of Defense Technology & Chinese Academy of Sciences & Inception Institute of Artificial Intelligence (IIAI) & Horizon Robotics Inc.
- arxiv: https://arxiv.org/abs/1904.02948
- github(official, Keras): https://github.com/liuwei16/CSP
Evading Real-Time Person Detectors by Adversarial T-shirt
https://arxiv.org/abs/1910.11099
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
https://arxiv.org/abs/1912.08661
Scale Match for Tiny Person Detection
- intro: WACV 2020
- arxiv: https://arxiv.org/abs/1912.10664
- github: https://github.com/ucas-vg/TinyBenchmark
Resisting the Distracting-factors in Pedestrian Detection
- intro: Beihang University & Arizona State University
- arxiv: https://arxiv.org/abs/2005.07344
SADet: Learning An Efficient and Accurate Pedestrian Detector
https://arxiv.org/abs/2007.13119
NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination
- intro: ACM MM 2020
- intro: Tencent Youtu Lab
- arxiv: https://arxiv.org/abs/2007.13376
Pedestrian Detection in a Crowd
Repulsion Loss: Detecting Pedestrians in a Crowd
- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1711.07752
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
- intro: ECCV 2018
- arxiv: https://arxiv.org/abs/1807.08407
Adaptive NMS: Refining Pedestrian Detection in a Crowd
- intro: CVPR 2019 oral
- arxiv: https://arxiv.org/abs/1904.03629
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes
- keywords: SUR-PED
- arxiv: https://arxiv.org/abs/1909.06826
Double Anchor R-CNN for Human Detection in a Crowd
- intro: Megvii Inc. (Face++) & Tsinghua University & Xi’an Jiaotong University & Zhejiang University
- arxiv: https://arxiv.org/abs/1909.09998
CSID: Center, Scale, Identity and Density-aware Pedestrian Detection in a Crowd
https://arxiv.org/abs/1910.09188
Semantic Head Enhanced Pedestrian Detection in a Crowd
https://arxiv.org/abs/1911.11985
Occluded Pedestrian Detection
Mask-Guided Attention Network for Occluded Pedestrian Detection
- intro: ICCV 2019
- arxiv: https://arxiv.org/abs/1910.06160
- github: https://github.com/Leotju/MGAN
Multispectral Pedestrian Detection
Multispectral Deep Neural Networks for Pedestrian Detection
- intro: BMVC 2016 oral
- arxiv: https://arxiv.org/abs/1611.02644
Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
- intro: State Key Lab of CAD&CG, Zhejiang University
- arxiv: https://arxiv.org/abs/1803.05347
Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
- intro: BMVC 2018
- arxiv: https://arxiv.org/abs/1808.04818
The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection
https://arxiv.org/abs/1901.02645
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
https://arxiv.org/abs/1902.05291
GFD-SSD: Gated Fusion Double SSD for Multispectral Pedestrian Detection
https://arxiv.org/abs/1903.06999
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
https://arxiv.org/abs/1904.03692
Vehicle Detection
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
- intro: ECCV 2016
- arxiv: http://arxiv.org/abs/1607.04564
Evolving Boxes for fast Vehicle Detection
Fine-Grained Car Detection for Visual Census Estimation
- intro: AAAI 2016
- arxiv: https://arxiv.org/abs/1709.02480
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
- intro: IEEE Transactions on Intelligent Transportation Systems (T-ITS)
- arxiv: https://arxiv.org/abs/1804.00433
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
- intro: UC Berkeley
- arxiv: https://arxiv.org/abs/1808.08603
Domain Randomization for Scene-Specific Car Detection and Pose Estimation
https://arxiv.org/abs/1811.05939
ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery
- intro: ECCV 2018, UAVision 2018
- arxiv: https://arxiv.org/abs/1811.06318
Traffic-Sign Detection
Traffic-Sign Detection and Classification in the Wild
- intro: CVPR 2016
- project page(code+dataset): http://cg.cs.tsinghua.edu.cn/traffic-sign/
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf
- code & model: http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/newdata0411.zip
Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data
- intro: CVPR 2017 workshop
- paper: http://openaccess.thecvf.com/content_cvpr_2017_workshops/w9/papers/Jensen_Evaluating_State-Of-The-Art_Object_CVPR_2017_paper.pdf
Detecting Small Signs from Large Images
- intro: IEEE Conference on Information Reuse and Integration (IRI) 2017 oral
- arxiv: https://arxiv.org/abs/1706.08574
Localized Traffic Sign Detection with Multi-scale Deconvolution Networks
https://arxiv.org/abs/1804.10428
Detecting Traffic Lights by Single Shot Detection
- intro: ITSC 2018
- arxiv: https://arxiv.org/abs/1805.02523
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
- intro: IEEE 15th Conference on Computer and Robot Vision
- arxiv: https://arxiv.org/abs/1806.07987
- demo: https://www.youtube.com/watch?v=_YmogPzBXOw&feature=youtu.be
Skeleton Detection
Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1703.02243
- github: https://github.com/KevinKecc/SRN
Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
https://arxiv.org/abs/1801.01849
Fruit Detection
Deep Fruit Detection in Orchards
Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
- intro: The Journal of Field Robotics in May 2016
- project page: http://confluence.acfr.usyd.edu.au/display/AGPub/
- arxiv: https://arxiv.org/abs/1610.08120
Shadow Detection
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
https://arxiv.org/abs/1709.09283
A+D-Net: Shadow Detection with Adversarial Shadow Attenuation
https://arxiv.org/abs/1712.01361
Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
https://arxiv.org/abs/1712.02478
Direction-aware Spatial Context Features for Shadow Detection
- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1712.04142
Direction-aware Spatial Context Features for Shadow Detection and Removal
- intro: The Chinese University of Hong Kong & The Hong Kong Polytechnic University
- arxiv: https://arxiv.org/abs/1805.04635
Others Detection
Deep Deformation Network for Object Landmark Localization
Fashion Landmark Detection in the Wild
- intro: ECCV 2016
- project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/FashionLandmarks.html
- arxiv: http://arxiv.org/abs/1608.03049
- github(Caffe): https://github.com/liuziwei7/fashion-landmarks
Deep Learning for Fast and Accurate Fashion Item Detection
- intro: Kuznech Inc.
- intro: MultiBox and Fast R-CNN
- paper: https://kddfashion2016.mybluemix.net/kddfashion_finalSubmissions/Deep%20Learning%20for%20Fast%20and%20Accurate%20Fashion%20Item%20Detection.pdf
OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)
Selfie Detection by Synergy-Constraint Based Convolutional Neural Network
- intro: IEEE SITIS 2016
- arxiv: https://arxiv.org/abs/1611.04357
Associative Embedding:End-to-End Learning for Joint Detection and Grouping
Deep Cuboid Detection: Beyond 2D Bounding Boxes
- intro: CMU & Magic Leap
- arxiv: https://arxiv.org/abs/1611.10010
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
Deep Learning Logo Detection with Data Expansion by Synthesising Context
Scalable Deep Learning Logo Detection
https://arxiv.org/abs/1803.11417
Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks
Automatic Handgun Detection Alarm in Videos Using Deep Learning
- arxiv: https://arxiv.org/abs/1702.05147
- results: https://github.com/SihamTabik/Pistol-Detection-in-Videos
Objects as context for part detection
https://arxiv.org/abs/1703.09529
Using Deep Networks for Drone Detection
- intro: AVSS 2017
- arxiv: https://arxiv.org/abs/1706.05726
Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1708.01642
Target Driven Instance Detection
https://arxiv.org/abs/1803.04610
DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion
https://arxiv.org/abs/1709.04577
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1710.06288
- github: https://github.com/SeokjuLee/VPGNet
Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants
https://arxiv.org/abs/1711.05128
ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos
- intro: WACV 2018
- arxiv: https://arxiv.org/abs/1801.02031
Deep Learning Object Detection Methods for Ecological Camera Trap Data
- intro: Conference of Computer and Robot Vision. University of Guelph
- arxiv: https://arxiv.org/abs/1803.10842
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
https://arxiv.org/abs/1806.05525
Towards End-to-End Lane Detection: an Instance Segmentation Approach
- arxiv: https://arxiv.org/abs/1802.05591
- github: https://github.com/MaybeShewill-CV/lanenet-lane-detection
iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
- intro: BMVC 2018
- project page: https://gaochen315.github.io/iCAN/
- arxiv: https://arxiv.org/abs/1808.10437
- github: https://github.com/vt-vl-lab/iCAN
Pose-aware Multi-level Feature Network for Human Object Interaction Detection
- intro: ICCV 2019
- arxiv: https://arxiv.org/abs/1909.08453
Densely Supervised Grasp Detector (DSGD)
https://arxiv.org/abs/1810.03962
Object Proposal
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
Scale-aware Pixel-wise Object Proposal Networks
- intro: IEEE Transactions on Image Processing
- arxiv: http://arxiv.org/abs/1601.04798
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
- intro: BMVC 2016. AttractioNet
- arxiv: https://arxiv.org/abs/1606.04446
- github: https://github.com/gidariss/AttractioNet
Learning to Segment Object Proposals via Recursive Neural Networks
Learning Detection with Diverse Proposals
- intro: CVPR 2017
- keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse Proposals (LDDP)
- arxiv: https://arxiv.org/abs/1704.03533
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
- keywords: product detection
- arxiv: https://arxiv.org/abs/1704.06752
Improving Small Object Proposals for Company Logo Detection
- intro: ICMR 2017
- arxiv: https://arxiv.org/abs/1704.08881
Open Logo Detection Challenge
- intro: BMVC 2018
- keywords: QMUL-OpenLogo
- project page: https://qmul-openlogo.github.io/
- arxiv: https://arxiv.org/abs/1807.01964
AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects
- intro: ACCV 2018 oral
- arxiv: https://arxiv.org/abs/1811.08728
- github: https://github.com/chwilms/AttentionMask
Localization
Beyond Bounding Boxes: Precise Localization of Objects in Images
- intro: PhD Thesis
- homepage: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.html
- phd-thesis: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.pdf
- github(“SDS using hypercolumns”): https://github.com/bharath272/sds
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
Weakly Supervised Object Localization Using Size Estimates
Active Object Localization with Deep Reinforcement Learning
- intro: ICCV 2015
- keywords: Markov Decision Process
- arxiv: https://arxiv.org/abs/1511.06015
Localizing objects using referring expressions
- intro: ECCV 2016
- keywords: LSTM, multiple instance learning (MIL)
- paper: http://www.umiacs.umd.edu/~varun/files/refexp-ECCV16.pdf
- github: https://github.com/varun-nagaraja/referring-expressions
LocNet: Improving Localization Accuracy for Object Detection
- intro: CVPR 2016 oral
- arxiv: http://arxiv.org/abs/1511.07763
- github: https://github.com/gidariss/LocNet
Learning Deep Features for Discriminative Localization
- homepage: http://cnnlocalization.csail.mit.edu/
- arxiv: http://arxiv.org/abs/1512.04150
- github(Tensorflow): https://github.com/jazzsaxmafia/Weakly_detector
- github: https://github.com/metalbubble/CAM
- github: https://github.com/tdeboissiere/VGG16CAM-keras
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
- intro: ECCV 2016
- project page: http://www.di.ens.fr/willow/research/contextlocnet/
- arxiv: http://arxiv.org/abs/1609.04331
- github: https://github.com/vadimkantorov/contextlocnet
Ensemble of Part Detectors for Simultaneous Classification and Localization
https://arxiv.org/abs/1705.10034
STNet: Selective Tuning of Convolutional Networks for Object Localization
https://arxiv.org/abs/1708.06418
Soft Proposal Networks for Weakly Supervised Object Localization
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1709.01829
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
- intro: ACM MM 2017
- arxiv: https://arxiv.org/abs/1709.08295
Tutorials / Talks
Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection
Towards Good Practices for Recognition & Detection
- intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
- slides: http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf
Work in progress: Improving object detection and instance segmentation for small objects
Object Detection with Deep Learning: A Review
https://arxiv.org/abs/1807.05511
Projects
Detectron
- intro: FAIR’s research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
- github: https://github.com/facebookresearch/Detectron
Detectron2
- intro: Detectron2 is FAIR’s next-generation platform for object detection and segmentation.
- github: https://github.com/facebookresearch/detectron2
MMDetection
- intro: MMDetection: Open MMLab Detection Toolbox and Benchmark
- arxiv: https://arxiv.org/abs/1906.07155
- github: https://github.com/open-mmlab/mmdetection
- docs: https://mmdetection.readthedocs.io/en/latest/
SimpleDet - A Simple and Versatile Framework for Object Detection and Instance Recognition
- intro: A Simple and Versatile Framework for Object Detection and Instance Recognition
- github: https://github.com/TuSimple/simpledet
AdelaiDet
- intro: AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
- github: https://github.com/aim-uofa/AdelaiDet/
TensorBox: a simple framework for training neural networks to detect objects in images
- intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
- github: https://github.com/Russell91/TensorBox
Object detection in torch: Implementation of some object detection frameworks in torch
Using DIGITS to train an Object Detection network
FCN-MultiBox Detector
- intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
- github: https://github.com/teaonly/FMD.torch
KittiBox: A car detection model implemented in Tensorflow.
- keywords: MultiNet
- intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
- github: https://github.com/MarvinTeichmann/KittiBox
Deformable Convolutional Networks + MST + Soft-NMS
How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow
- blog: https://towardsdatascience.com/how-to-build-a-real-time-hand-detector-using-neural-networks-ssd-on-tensorflow-d6bac0e4b2ce
- github: https://github.com//victordibia/handtracking
Metrics for object detection
- intro: Most popular metrics used to evaluate object detection algorithms
- github: https://github.com/rafaelpadilla/Object-Detection-Metrics
MobileNetv2-SSDLite
- intro: Caffe implementation of SSD and SSDLite detection on MobileNetv2, converted from tensorflow.
- github: https://github.com/chuanqi305/MobileNetv2-SSDLite
Leaderboard
Detection Results: VOC2012
- intro: Competition “comp4” (train on additional data)
- homepage: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4
Tools
BeaverDam: Video annotation tool for deep learning training labels
https://github.com/antingshen/BeaverDam
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.
Blogs
Convolutional Neural Networks for Object Detection
http://rnd.azoft.com/convolutional-neural-networks-object-detection/
Introducing automatic object detection to visual search (Pinterest)
- keywords: Faster R-CNN
- blog: https://engineering.pinterest.com/blog/introducing-automatic-object-detection-visual-search
- demo: https://engineering.pinterest.com/sites/engineering/files/Visual%20Search%20V1%20-%20Video.mp4
- review: https://news.developer.nvidia.com/pinterest-introduces-the-future-of-visual-search/?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D
Deep Learning for Object Detection with DIGITS
Analyzing The Papers Behind Facebook’s Computer Vision Approach
- keywords: DeepMask, SharpMask, MultiPathNet
- blog: https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook’s-Computer-Vision-Approach/
Easily Create High Quality Object Detectors with Deep Learning
- intro: dlib v19.2
- blog: http://blog.dlib.net/2016/10/easily-create-high-quality-object.html
How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit
- blog: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/
- github: https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN
Object Detection in Satellite Imagery, a Low Overhead Approach
- part 1: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7#.2csh4iwx9
- part 2: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92#.f9b7dgf64
You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks
- part 1: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571#.fmmi2o3of
- part 2: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588#.nwzarsz1t
Faster R-CNN Pedestrian and Car Detection
- blog: https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
- ipn: https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0#file-demo_faster_rcnn_notebook-ipynb
- github: https://github.com/bigsnarfdude/Faster-RCNN_TF
Small U-Net for vehicle detection
Region of interest pooling explained
- blog: https://deepsense.io/region-of-interest-pooling-explained/
- github: https://github.com/deepsense-io/roi-pooling
Supercharge your Computer Vision models with the TensorFlow Object Detection API
- blog: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
- github: https://github.com/tensorflow/models/tree/master/object_detection
Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning
One-shot object detection
http://machinethink.net/blog/object-detection/
An overview of object detection: one-stage methods
https://www.jeremyjordan.me/object-detection-one-stage/
deep learning object detection
- intro: A paper list of object detection using deep learning.
- arxiv: https://github.com/hoya012/deep_learning_object_detection
