目标检测 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

文章目录

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: 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

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

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

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

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

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

目标检测 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

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

目标检测 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

目标检测 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