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目标檢測 object-detection

This is a list of awesome articles about object detection.

from: 引自GitHub

R-CNN

Fast R-CNN

Faster 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

MegNet

RefineNet

DetNet

SSOD

3D Object Detection

ZSD（Zero-Shot Object Detection）

OSD（One-Shot object Detection）

Other

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

Papers&Codes

文章目錄

    • Papers&Codes
      • 1 R-CNN
      • 2 Fast R-CNN
        • 2.1 Fast R-CNN
        • 2.2 A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
      • Faster R-CNN
        • Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
        • R-CNN minus R
        • Faster R-CNN in MXNet with distributed implementation and data parallelization
        • Contextual Priming and Feedback for Faster R-CNN
        • An Implementation of Faster RCNN with Study for Region Sampling
        • Interpretable R-CNN
      • Light-Head R-CNN
        • Light-Head R-CNN: In Defense of Two-Stage Object Detector
      • Cascade R-CNN
        • Cascade R-CNN: Delving into High Quality Object Detection
      • SPP-Net
        • Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
        • DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
        • Object Detectors Emerge in Deep Scene CNNs
        • segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
        • Object Detection Networks on Convolutional Feature Maps
        • Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
        • DeepBox: Learning Objectness with Convolutional Networks
      • YOLO
        • darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
        • Start Training YOLO with Our Own Data
        • YOLO: Core ML versus MPSNNGraph
        • TensorFlow YOLO object detection on Android
        • Computer Vision in iOS – Object Detection
      • YOLOv2
        • YOLO9000: Better, Faster, Stronger
        • 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
        • YOLO v2 Bounding Box Tool
        • Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
        • Object detection at 200 Frames Per Second
        • Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras
        • OmniDetector: With Neural Networks to Bounding Boxes
      • YOLOv3
        • YOLOv3: An Incremental Improvement
      • YOLT
        • You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery
      • SSD
        • SSD: Single Shot MultiBox Detector
      • DSSD
        • DSSD : Deconvolutional Single Shot Detector
        • Enhancement of SSD by concatenating feature maps for object detection
        • Context-aware Single-Shot Detector
        • Feature-Fused SSD: Fast Detection for Small Objects
      • FSSD
        • FSSD: Feature Fusion Single Shot Multibox Detector
        • Weaving Multi-scale Context for Single Shot Detector
      • ESSD
        • Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
        • Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
      • MDSSD
        • MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects
      • Pelee
        • Pelee: A Real-Time Object Detection System on Mobile Devices
      • Fire SSD
        • Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device
      • R-FCN
        • R-FCN: Object Detection via Region-based Fully Convolutional Networks
        • R-FCN-3000 at 30fps: Decoupling Detection and Classification
        • Recycle deep features for better object detection
      • FPN
        • Feature Pyramid Networks for Object Detection
        • Action-Driven Object Detection with Top-Down Visual Attentions
        • Beyond Skip Connections: Top-Down Modulation for Object Detection
        • Wide-Residual-Inception Networks for Real-time Object Detection
        • Attentional Network for Visual Object Detection
        • Learning Chained Deep Features and Classifiers for Cascade in Object Detection
        • DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
        • Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
        • Spatial Memory for Context Reasoning in Object Detection
        • Accurate Single Stage Detector Using Recurrent Rolling Convolution
        • Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
        • LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
        • Point Linking Network for Object Detection
        • 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
        • Mimicking Very Efficient Network for Object Detection
        • Residual Features and Unified Prediction Network for Single Stage Detection
        • Deformable Part-based Fully Convolutional Network for Object Detection
        • Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
        • Recurrent Scale Approximation for Object Detection in CNN
      • DSOD
        • DSOD: Learning Deeply Supervised Object Detectors from Scratch
        • Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
      • RetinaNet
        • Focal Loss for Dense Object Detection
        • CoupleNet: Coupling Global Structure with Local Parts for Object Detection
        • Incremental Learning of Object Detectors without Catastrophic Forgetting
        • Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
        • StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
        • Dynamic Zoom-in Network for Fast Object Detection in Large Images
        • Zero-Annotation Object Detection with Web Knowledge Transfer
      • MegDet
        • MegDet: A Large Mini-Batch Object Detector
        • Receptive Field Block Net for Accurate and Fast Object Detection
        • An Analysis of Scale Invariance in Object Detection - SNIP
        • Feature Selective Networks for Object Detection
        • Learning a Rotation Invariant Detector with Rotatable Bounding Box
        • Scalable Object Detection for Stylized Objects
        • Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
        • Deep Regionlets for Object Detection
        • Training and Testing Object Detectors with Virtual Images
        • Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
        • Spot the Difference by Object Detection
        • Localization-Aware Active Learning for Object Detection
        • Object Detection with Mask-based Feature Encoding
        • LSTD: A Low-Shot Transfer Detector for Object Detection
        • Domain Adaptive Faster R-CNN for Object Detection in the Wild
        • Pseudo Mask Augmented Object Detection
        • Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
        • Learning Region Features for Object Detection
        • Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
        • Object Detection for Comics using Manga109 Annotations
        • 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
        • Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
        • Robust Physical Adversarial Attack on Faster R-CNN Object Detector
      • RefineNet
        • Single-Shot Refinement Neural Network for Object Detection
      • DetNet
        • DetNet: A Backbone network for Object Detection
      • SSOD
        • Self-supervisory Signals for Object Discovery and Detection
      • 3D Object Detection
        • LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs
      • ZSD
        • Zero-Shot Detection
        • Zero-Shot Object Detection
        • Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
        • Zero-Shot Object Detection by Hybrid Region Embedding
      • OSD
        • One-Shot Object Detection
      • 2018
        • MetaAnchor: Learning to Detect Objects with Customized Anchors
        • Relation Network for Object Detection
        • Quantization Mimic: Towards Very Tiny CNN for Object Detection
        • Learning Rich Features for Image Manipulation Detection
        • SNIPER: Efficient Multi-Scale Training
        • Soft Sampling for Robust Object Detection
        • Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria

1 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

2 Fast R-CNN

2.1 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

2.2 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

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

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/˜wlouyang/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

目标檢測 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

目标檢測 object-detection

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/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

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

目标檢測 object-detection

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/~wliu/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

目标檢測 object-detection

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

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

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

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

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

https://arxiv.org/abs/1803.11316

Transferring Common-Sense Knowledge for Object Detection

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

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

3D Object Detection

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

2018

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

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