object-detection

[TOC]

This is a list of awesome articles about object detection. If you want to read the paper according to time, you can refer to Date.

• R-CNN
• Fast R-CNN
• Faster R-CNN
• Mask R-CNN
• Light-Head R-CNN
• Cascade R-CNN
• SPP-Net
• YOLO
• YOLOv2
• YOLOv3
• YOLT
• SSD
• DSSD
• FSSD
• ESSD
• MDSSD
• Pelee
• Fire SSD
• R-FCN
• FPN
• DSOD
• RetinaNet
• MegDet
• RefineNet
• DetNet
• SSOD
• CornerNet
• M2Det
• 3D Object Detection
• ZSD（Zero-Shot Object Detection）
• OSD（One-Shot object Detection）
• Weakly Supervised Object Detection
• Softer-NMS
• 2018
• 2019
• Other

Based on handong1587's github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

Survey

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

• intro: awesome

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

《Deep Learning for Generic Object Detection: A Survey》

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

Papers&Codes

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

• intro: R-CNN
• arxiv: http://arxiv.org/abs/1311.2524
• supp: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
• slides: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
• slides: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
• github: https://github.com/rbgirshick/rcnn
• notes: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
• caffe-pr("Make R-CNN the Caffe detection example"): BVLC/caffe#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: rbgirshick/fast-rcnn#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

weiliu89/caffe#327

DSSD

DSSD : Deconvolutional Single Shot Detector

• intro: UNC Chapel Hill & Amazon Inc
• arxiv: https://arxiv.org/abs/1701.06659
• github: https://github.com/chengyangfu/caffe/tree/dssd
• github: https://github.com/MTCloudVision/mxnet-dssd
• demo: http://120.52.72.53/www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd_lalaland.mp4

Enhancement of SSD by concatenating feature maps for object detection

• intro: rainbow SSD (R-SSD)
• arxiv: https://arxiv.org/abs/1705.09587

Context-aware Single-Shot Detector

• keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
• arxiv: https://arxiv.org/abs/1707.08682

Feature-Fused SSD: Fast Detection for Small Objects

https://arxiv.org/abs/1709.05054

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

https://arxiv.org/abs/1712.00960

Weaving Multi-scale Context for Single Shot Detector

• intro: WeaveNet
• keywords: fuse multi-scale information
• arxiv: https://arxiv.org/abs/1712.03149

ESSD

Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network

https://arxiv.org/abs/1801.05918

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

https://arxiv.org/abs/1802.06488

MDSSD

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

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

Pelee

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

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

• intro: (ICLR 2018 workshop track)

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

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

Fire SSD

Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device

• intro:low cost, fast speed and high mAP on factor edge computing devices

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

R-FCN

R-FCN: Object Detection via Region-based Fully Convolutional Networks

• arxiv: http://arxiv.org/abs/1605.06409
• github: https://github.com/daijifeng001/R-FCN
• github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn
• github: https://github.com/Orpine/py-R-FCN
• github: https://github.com/PureDiors/pytorch_RFCN
• github: https://github.com/bharatsingh430/py-R-FCN-multiGPU
• github: https://github.com/xdever/RFCN-tensorflow

R-FCN-3000 at 30fps: Decoupling Detection and Classification

https://arxiv.org/abs/1712.01802

Recycle deep features for better object detection

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

FPN

Feature Pyramid Networks for Object Detection

• intro: Facebook AI Research
• arxiv: https://arxiv.org/abs/1612.03144

Action-Driven Object Detection with Top-Down Visual Attentions

• arxiv: https://arxiv.org/abs/1612.06704

Beyond Skip Connections: Top-Down Modulation for Object Detection

• intro: CMU & UC Berkeley & Google Research
• arxiv: https://arxiv.org/abs/1612.06851

Wide-Residual-Inception Networks for Real-time Object Detection

• intro: Inha University
• arxiv: https://arxiv.org/abs/1702.01243

Attentional Network for Visual Object Detection

• intro: University of Maryland & Mitsubishi Electric Research Laboratories
• arxiv: https://arxiv.org/abs/1702.01478

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

• keykwords: CC-Net
• intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
• arxiv: https://arxiv.org/abs/1702.07054

DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling

• intro: ICCV 2017 (poster)
• arxiv: https://arxiv.org/abs/1703.10295

Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1704.03944

Spatial Memory for Context Reasoning in Object Detection

• arxiv: https://arxiv.org/abs/1704.04224

Accurate Single Stage Detector Using Recurrent Rolling Convolution

• intro: CVPR 2017. SenseTime
• keywords: Recurrent Rolling Convolution (RRC)
• arxiv: https://arxiv.org/abs/1704.05776
• github: https://github.com/xiaohaoChen/rrc_detection

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

https://arxiv.org/abs/1704.05775

LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems

• intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc
• arxiv: https://arxiv.org/abs/1705.05922

Point Linking Network for Object Detection

• intro: Point Linking Network (PLN)
• arxiv: https://arxiv.org/abs/1706.03646

Perceptual Generative Adversarial Networks for Small Object Detection

https://arxiv.org/abs/1706.05274

Few-shot Object Detection

https://arxiv.org/abs/1706.08249

Yes-Net: An effective Detector Based on Global Information

https://arxiv.org/abs/1706.09180

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

https://arxiv.org/abs/1706.10217

Towards lightweight convolutional neural networks for object detection

https://arxiv.org/abs/1707.01395

RON: Reverse Connection with Objectness Prior Networks for Object Detection

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1707.01691
• github: https://github.com/taokong/RON

Mimicking Very Efficient Network for Object Detection

• intro: CVPR 2017. SenseTime & Beihang University
• paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf

Residual Features and Unified Prediction Network for Single Stage Detection

https://arxiv.org/abs/1707.05031

Deformable Part-based Fully Convolutional Network for Object Detection

• intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
• arxiv: https://arxiv.org/abs/1707.06175

Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1707.06399

Recurrent Scale Approximation for Object Detection in CNN

• intro: ICCV 2017
• keywords: Recurrent Scale Approximation (RSA)
• arxiv: https://arxiv.org/abs/1707.09531
• github: https://github.com/sciencefans/RSA-for-object-detection

DSOD

DSOD: Learning Deeply Supervised Object Detectors from Scratch

• intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
• arxiv: https://arxiv.org/abs/1708.01241
• github: https://github.com/szq0214/DSOD
• github:https://github.com/Windaway/DSOD-Tensorflow
• github:https://github.com/chenyuntc/dsod.pytorch

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

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

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

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

Object Detection from Scratch with Deep Supervision

• intro: This is an extended version of DSOD
• arXiv: https://arxiv.org/abs/1809.09294

RetinaNet

Focal Loss for Dense Object Detection

• intro: ICCV 2017 Best student paper award. Facebook AI Research
• keywords: RetinaNet
• arxiv: https://arxiv.org/abs/1708.02002

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1708.02863

Incremental Learning of Object Detectors without Catastrophic Forgetting

• intro: ICCV 2017. Inria
• arxiv: https://arxiv.org/abs/1708.06977

Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection

https://arxiv.org/abs/1709.04347

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

https://arxiv.org/abs/1709.05788

Dynamic Zoom-in Network for Fast Object Detection in Large Images

https://arxiv.org/abs/1711.05187

Zero-Annotation Object Detection with Web Knowledge Transfer

• intro: NTU, Singapore & Amazon
• keywords: multi-instance multi-label domain adaption learning framework
• arxiv: https://arxiv.org/abs/1711.05954

MegDet

MegDet: A Large Mini-Batch Object Detector

• intro: Peking University & Tsinghua University & Megvii Inc
• arxiv: https://arxiv.org/abs/1711.07240

Receptive Field Block Net for Accurate and Fast Object Detection

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

An Analysis of Scale Invariance in Object Detection - SNIP

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

Feature Selective Networks for Object Detection

https://arxiv.org/abs/1711.08879

Learning a Rotation Invariant Detector with Rotatable Bounding Box

• arxiv: https://arxiv.org/abs/1711.09405
• github: https://github.com/liulei01/DRBox

Scalable Object Detection for Stylized Objects

• intro: Microsoft AI & Research Munich
• arxiv: https://arxiv.org/abs/1711.09822

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

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

Deep Regionlets for Object Detection

• keywords: region selection network, gating network
• arxiv: https://arxiv.org/abs/1712.02408

Training and Testing Object Detectors with Virtual Images

• intro: IEEE/CAA Journal of Automatica Sinica
• arxiv: https://arxiv.org/abs/1712.08470

Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video

• keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
• arxiv: https://arxiv.org/abs/1712.08832

Spot the Difference by Object Detection

• intro: Tsinghua University & JD Group
• arxiv: https://arxiv.org/abs/1801.01051

Localization-Aware Active Learning for Object Detection

• arxiv: https://arxiv.org/abs/1801.05124

Object Detection with Mask-based Feature Encoding

• arxiv: https://arxiv.org/abs/1802.03934

LSTD: A Low-Shot Transfer Detector for Object Detection

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1803.01529

Pseudo Mask Augmented Object Detection

https://arxiv.org/abs/1803.05858

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://arxiv.org/abs/1803.06799

Learning Region Features for Object Detection

• intro: Peking University & MSRA
• arxiv: https://arxiv.org/abs/1803.07066

Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection

• intro: Singapore Management University & Zhejiang University
• arxiv: https://arxiv.org/abs/1803.08208

Object Detection for Comics using Manga109 Annotations

• intro: University of Tokyo & National Institute of Informatics, Japan
• arxiv: https://arxiv.org/abs/1803.08670

Task-Driven Super Resolution: Object Detection in Low-resolution Images

• arxiv: https://arxiv.org/abs/1803.11316

Transferring Common-Sense Knowledge for Object Detection

• arxiv: https://arxiv.org/abs/1804.01077

Multi-scale Location-aware Kernel Representation for Object Detection

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1804.00428
• github: https://github.com/Hwang64/MLKP

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

• intro: National University of Defense Technology
• arxiv: https://arxiv.org/abs/1804.04606

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

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

RefineNet

Single-Shot Refinement Neural Network for Object Detection

• intro: CVPR 2018

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

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

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

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

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

DetNet

DetNet: A Backbone network for Object Detection

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

SSOD

Self-supervisory Signals for Object Discovery and Detection

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

CornerNet

CornerNet: Detecting Objects as Paired Keypoints

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

M2Det

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

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

3D Object Detection

3D Backbone Network for 3D Object Detection

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

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

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

ZSD

Zero-Shot Detection

• intro: Australian National University
• keywords: YOLO
• arxiv: https://arxiv.org/abs/1803.07113

Zero-Shot Object Detection

• arxiv: https://arxiv.org/abs/1804.04340

Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

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

Zero-Shot Object Detection by Hybrid Region Embedding

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

OSD

One-Shot Object Detection

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

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

Weakly Supervised Object Detection

Weakly Supervised Object Detection in Artworks

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

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

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

Softer-NMS

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

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

2019

Object Detection based on Region Decomposition and Assembly

• intro: AAAI 2019

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

Bottom-up Object Detection by Grouping Extreme and Center Points

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

ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

• intro: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

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

Consistent Optimization for Single-Shot Object Detection

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

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

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

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

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

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

Region Proposal by Guided Anchoring

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

Scale-Aware Trident Networks for Object Detection

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

2018

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

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

Strong-Weak Distribution Alignment for Adaptive Object Detection

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

AutoFocus: Efficient Multi-Scale Inference

• intro: AutoFocus obtains an mAP of 47.9% (68.3% at 50% overlap) on the COCO test-dev set while processing 6.4 images per second on a Titan X (Pascal) GPU
• arXiv: https://arxiv.org/abs/1812.01600

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

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

SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection

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

Grid R-CNN

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

Deformable ConvNets v2: More Deformable, Better Results

• intro: Microsoft Research Asia

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

Anchor Box Optimization for Object Detection

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

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

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

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

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

Learning RoI Transformer for Detecting Oriented Objects in Aerial Images

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

Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

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

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

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

Gradient Harmonized Single-stage Detector

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

CFENet: Object Detection with Comprehensive Feature Enhancement Module

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

DeRPN: Taking a further step toward more general object detection

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

Hybrid Knowledge Routed Modules for Large-scale Object Detection

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

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

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

Deep Feature Pyramid Reconfiguration for Object Detection

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

Unsupervised Hard Example Mining from Videos for Improved Object Detection

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

Acquisition of Localization Confidence for Accurate Object Detection

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

Toward Scale-Invariance and Position-Sensitive Region Proposal Networks

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

MetaAnchor: Learning to Detect Objects with Customized Anchors

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

Relation Network for Object Detection

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

Quantization Mimic: Towards Very Tiny CNN for Object Detection

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

Learning Rich Features for Image Manipulation Detection

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

SNIPER: Efficient Multi-Scale Training

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

Soft Sampling for Robust Object Detection

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

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

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

Other

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

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