2019年目标检测论文汇总

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.