目标检测神文，非常全而且持续在更新。转发自：https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html，如有侵权联系删除。
更新时间：
20190226 暂时停更
不再更新，最新检测文章请移步：https://blog.csdn.net/hw5226349/article/details/88733364
我会跟进原作者博客持续更新，加入自己对目标检测领域的一些新研究及论文解读。博客根据需求直接进行关键字搜索，例如2018，可找到最新论文。

Method	backbone	test size	VOC2007	VOC2010	VOC2012	ILSVRC 2013	MSCOCO 2015	Speed
OverFeat						24.3%
R-CNN	AlexNet		58.5%	53.7%	53.3%	31.4%
R-CNN	VGG17		66.0%
SPP_net	ZF-5		54.2%			31.84%
DeepID-Net			64.1%			50.3%
NoC			73.3%		68.8%
Fast-RCNN	VGG16		70.0%	68.8%	68.4%		19.7%(@[0.5-0.95]), 35.9%(@0.5)
MR-CNN			78.2%		73.9%
Faster-RCNN	VGG16		78.8%		75.9%		21.9%(@[0.5-0.95]), 42.7%(@0.5)	198ms
Faster-RCNN	ResNet101		85.6%		83.8%		37.4%(@[0.5-0.95]), 59.0%(@0.5)
YOLO			63.4%		57.9%			45 fps
YOLO	VGG-16		66.4%					21 fps
YOLOv2		448x448	78.6%		73.4%		21.6%(@[0.5-0.95]), 44.0%(@0.5)	40 fps
SSD	VGG16	300x300	77.2%		75.8%		25.1%(@[0.5-0.95]), 43.1%(@0.5)	46 fps
SSD	VGG16	512x512	79.8%		78.5%		28.8%(@[0.5-0.95]), 48.5%(@0.5)	19 fps
SSD	ResNet101	300x300					28.0%(@[0.5-0.95])	16 fps
SSD	ResNet101	512x512					31.2%(@[0.5-0.95])	8 fps
DSSD	ResNet101	300x300					28.0%(@[0.5-0.95])	8 fps
DSSD	ResNet101	500x500					33.2%(@[0.5-0.95])	6 fps
ION			79.2%		76.4%
CRAFT			75.7%		71.3%	48.5%
OHEM			78.9%		76.3%		25.5%(@[0.5-0.95]), 45.9%(@0.5)
R-FCN	ResNet50		77.4%					0.12sec(K40), 0.09sec(TitianX)
R-FCN	ResNet101		79.5%					0.17sec(K40), 0.12sec(TitianX)
R-FCN(ms train)	ResNet101		83.6%		82.0%		31.5%(@[0.5-0.95]), 53.2%(@0.5)
PVANet 9.0			84.9%		84.2%			750ms(CPU), 46ms(TitianX)
RetinaNet	ResNet101-FPN
Light-Head R-CNN	Xception*	800/1200					31.5%@[0.5:0.95]	95 fps
Light-Head R-CNN	Xception*	700/1100					30.7%@[0.5:0.95]	102 fps
STDN			80.9 (07+12)
RefineDet			83.8 (07+12)		83.5 (07++12)		41.8
SNIP							45.7
Relation-Network							32.5
Cascade R-CNN							42.8
MLKP			80.6 (07+12)		77.2 (07++12)		28.6
Fitness-NMS							41.8
RFBNet			82.2 (07+12)
CornerNet							42.1
PFPNet			84.1 (07+12)		83.7 (07++12)		39.4
Pelee			70.9 (07+12)
HKRM			78.8 (07+12)				37.8
M2Det							44.2
SIN			76.0 (07+12)		73.1 (07++12)		23.2

Papers

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损失函数

[CVPR2019] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

• arxiv: https://arxiv.org/abs/1902.09630

Deep Neural Networks for Object Detection

• paper: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

• arxiv: http://arxiv.org/abs/1312.6229
• github: https://github.com/sermanet/OverFeat
• code: http://cilvr.nyu.edu/doku.php?id=software:overfeat:start

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R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

• intro: R-CNN
• arxiv: http://arxiv.org/abs/1311.2524
• supp: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
• slides: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
• slides: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
• github: https://github.com/rbgirshick/rcnn
• notes: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
• caffe-pr(“Make R-CNN the Caffe detection example”): https://github.com/BVLC/caffe/pull/482

---

Fast R-CNN

Fast R-CNN

• arxiv: http://arxiv.org/abs/1504.08083
• slides: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
• github: https://github.com/rbgirshick/fast-rcnn
• github(COCO-branch): https://github.com/rbgirshick/fast-rcnn/tree/coco
• webcam demo: https://github.com/rbgirshick/fast-rcnn/pull/29
• notes: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
• notes: http://blog.csdn.net/linj_m/article/details/48930179
• github(“Fast R-CNN in MXNet”): https://github.com/precedenceguo/mx-rcnn
• github: https://github.com/mahyarnajibi/fast-rcnn-torch
• github: https://github.com/apple2373/chainer-simple-fast-rnn
• github: https://github.com/zplizzi/tensorflow-fast-rcnn

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1704.03414
• paper: http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf
• github(Caffe): https://github.com/xiaolonw/adversarial-frcnn

Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

• intro: NIPS 2015
• arxiv: http://arxiv.org/abs/1506.01497
• gitxiv: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
• slides: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
• github(official, Matlab): https://github.com/ShaoqingRen/faster_rcnn
• github: https://github.com/rbgirshick/py-faster-rcnn
• github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn
• github: https://github.com//jwyang/faster-rcnn.pytorch
• github: https://github.com/mitmul/chainer-faster-rcnn
• github: https://github.com/andreaskoepf/faster-rcnn.torch
• github: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
• github: https://github.com/smallcorgi/Faster-RCNN_TF
• github: https://github.com/CharlesShang/TFFRCNN
• github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
• github: https://github.com/yhenon/keras-frcnn
• github: https://github.com/Eniac-Xie/faster-rcnn-resnet
• github(C++): https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev

R-CNN minus R

• intro: BMVC 2015
• arxiv: http://arxiv.org/abs/1506.06981

Faster R-CNN in MXNet with distributed implementation and data parallelization

• 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

[AAAI2019]Object Detection based on Region Decomposition and Assembly

• intro: AAAI2019,区域分解组装
• arxiv: https://arxiv.org/abs/1901.08225
• translate: https://zhuanlan.zhihu.com/p/58951221 论文翻译

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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(official, Tensorflow): https://github.com/zengarden/light_head_rcnn
• github: https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet_v1_101_rfcn_light.py#L784

##Cascade R-CNN

Cascade R-CNN: Delving into High Quality Object Detection

• intro: CVPR 2018. UC San Diego
• arxiv: https://arxiv.org/abs/1712.00726
• github(Caffe, official): https://github.com/zhaoweicai/cascade-rcnn

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MultiBox

Scalable Object Detection using Deep Neural Networks

• intro: first MultiBox. Train a CNN to predict Region of Interest.
• arxiv: http://arxiv.org/abs/1312.2249
• github: https://github.com/google/multibox
• blog: https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html

Scalable, High-Quality Object Detection

• intro: second MultiBox
• arxiv: http://arxiv.org/abs/1412.1441
• github: https://github.com/google/multibox

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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

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MR-CNN

Object detection via a multi-region & semantic segmentation-aware CNN model

• intro: ICCV 2015. MR-CNN
• arxiv: http://arxiv.org/abs/1505.01749
• github: https://github.com/gidariss/mrcnn-object-detection
• notes: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
• notes: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/

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YOLO

You Only Look Once: Unified, Real-Time Object Detection

• arxiv: http://arxiv.org/abs/1506.02640
• code: http://pjreddie.com/darknet/yolo/
• github: https://github.com/pjreddie/darknet
• blog: https://pjreddie.com/publications/yolo/
• slides: https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p
• 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/
• github(Chainer): https://github.com/leetenki/YOLOv2
• github(Keras): https://github.com/allanzelener/YAD2K
• github(PyTorch): https://github.com/longcw/yolo2-pytorch
• github(Tensorflow): https://github.com/hizhangp/yolo_tensorflow
• github(Windows): https://github.com/AlexeyAB/darknet
• github: https://github.com/choasUp/caffe-yolo9000
• github: https://github.com/philipperemy/yolo-9000

darknet_scripts

• intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
• github: https://github.com/Jumabek/darknet_scripts

Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2

• github: https://github.com/AlexeyAB/Yolo_mark

LightNet: Bringing pjreddie’s DarkNet out of the shadows

• github: 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

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YOLOv3

YOLOv3: An Incremental Improvement

• project page: https://pjreddie.com/darknet/yolo/
• arxiv: https://arxiv.org/abs/1804.02767
• github: https://github.com/DeNA/PyTorch_YOLOv3
• github: https://github.com/eriklindernoren/PyTorch-YOLOv3

YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers

• arxiv：https://arxiv.org/abs/1811.05588

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AttentionNet: Aggregating Weak Directions for Accurate Object Detection

• intro: ICCV 2015
• intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection task
• arxiv: http://arxiv.org/abs/1506.07704
• slides: https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf
• slides: http://image-net.org/challenges/talks/lunit-kaist-slide.pdf

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DenseBox

DenseBox: Unifying Landmark Localization with End to End Object Detection

• arxiv: http://arxiv.org/abs/1509.04874
• demo: http://pan.baidu.com/s/1mgoWWsS
• KITTI result: http://www.cvlibs.net/datasets/kitti/eval_object.php

---

SSD

SSD: Single Shot MultiBox Detector

• intro: ECCV 2016 Oral
• arxiv: http://arxiv.org/abs/1512.02325
• paper: http://www.cs.unc.edu/~wliu/papers/ssd.pdf
• slides: http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.pdf
• github(Official): https://github.com/weiliu89/caffe/tree/ssd
• video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973
• github: https://github.com/zhreshold/mxnet-ssd
• github: https://github.com/zhreshold/mxnet-ssd.cpp
• github: https://github.com/rykov8/ssd_keras
• github: https://github.com/balancap/SSD-Tensorflow
• github: https://github.com/amdegroot/ssd.pytorch
• github(Caffe): https://github.com/chuanqi305/MobileNet-SSD
  What’s the diffience in performance between this new code you pushed and the previous code? #327
  https://github.com/weiliu89/caffe/issues/327

---

DSSD

DSSD : Deconvolutional Single Shot Detector

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

Enhancement of SSD by concatenating feature maps for object detection

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

Context-aware Single-Shot Detector

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

Feature-Fused SSD: Fast Detection for Small Objects

https://arxiv.org/abs/1709.05054

---

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

https://arxiv.org/abs/1712.00960

Weaving Multi-scale Context for Single Shot Detector

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

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ESSD

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

• arxiv: https://arxiv.org/abs/1801.05918

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

• arxiv: https://arxiv.org/abs/1802.06488

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

• intro: Zhengzhou University
• arxiv: https://arxiv.org/abs/1805.07009

---

Inside-Outside Net (ION)

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

• intro: “0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it”.
• arxiv: http://arxiv.org/abs/1512.04143
• slides: http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf
• coco-leaderboard: http://mscoco.org/dataset/#detections-leaderboard

Adaptive Object Detection Using Adjacency and Zoom Prediction

• intro: CVPR 2016. AZ-Net
• arxiv: http://arxiv.org/abs/1512.07711
• github: https://github.com/luyongxi/az-net
• youtube: https://www.youtube.com/watch?v=YmFtuNwxaNM

G-CNN: an Iterative Grid Based Object Detector

• arxiv: http://arxiv.org/abs/1512.07729

---

Factors in Finetuning Deep Model for object detection

Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution

• intro: CVPR 2016.rank 3rd for provided data and 2nd for external data on ILSVRC 2015 object detection
• project page: http://www.ee.cuhk.edu.hk/~wlouyang/projects/ImageNetFactors/CVPR16.html
• arxiv: http://arxiv.org/abs/1601.05150

We don’t need no bounding-boxes: Training object class detectors using only human verification

• arxiv: http://arxiv.org/abs/1602.08405

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

• arxiv: http://arxiv.org/abs/1604.00600

A MultiPath Network for Object Detection

• intro: BMVC 2016. Facebook AI Research (FAIR)
• arxiv: http://arxiv.org/abs/1604.02135
• github: https://github.com/facebookresearch/multipathnet

---

CRAFT

CRAFT Objects from Images

• intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN
• project page: http://byangderek.github.io/projects/craft.html
• arxiv: https://arxiv.org/abs/1604.03239
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_CRAFT_Objects_From_CVPR_2016_paper.pdf
• github: https://github.com/byangderek/CRAFT

---

OHEM

Training Region-based Object Detectors with Online Hard Example Mining

• intro: CVPR 2016 Oral. Online hard example mining (OHEM)
• arxiv: http://arxiv.org/abs/1604.03540
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.pdf
• github(Official): https://github.com/abhi2610/ohem
• author page: http://abhinav-shrivastava.info/

S-OHEM: Stratified Online Hard Example Mining for Object Detection

• arxiv: https://arxiv.org/abs/1705.02233

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

• intro: CVPR 2016
• keywords: scale-dependent pooling (SDP), cascaded rejection classifiers (CRC)
• paper: http://www-personal.umich.edu/~wgchoi/SDP-CRC_camready.pdf

---

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

• arxiv: https://arxiv.org/abs/1712.01802

Recycle deep features for better object detection

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

---

MS-CNN

A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

• intro: ECCV 2016
• intro: 640×480: 15 fps, 960×720: 8 fps
• arxiv: http://arxiv.org/abs/1607.07155
• github: https://github.com/zhaoweicai/mscnn
• poster: http://www.eccv2016.org/files/posters/P-2B-38.pdf

Multi-stage Object Detection with Group Recursive Learning

• intro: VOC2007: 78.6%, VOC2012: 74.9%
• arxiv: http://arxiv.org/abs/1608.05159

Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

• intro: WACV 2017. SubCNN
• arxiv: http://arxiv.org/abs/1604.04693
• github: https://github.com/tanshen/SubCNN

---

PVANET

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

• intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). Continuation of arXiv:1608.08021
• arxiv: https://arxiv.org/abs/1611.08588
• github: https://github.com/sanghoon/pva-faster-rcnn
• leaderboard(PVANet 9.0): http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

---

GBD-Net

Gated Bi-directional CNN for Object Detection

• intro: The Chinese University of Hong Kong & Sensetime Group Limited
• paper: http://link.springer.com/chapter/10.1007/978-3-319-46478-7_22
• mirror: https://pan.baidu.com/s/1dFohO7v

Crafting GBD-Net for Object Detection

• intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo
• intro: gated bi-directional CNN (GBD-Net)
• arxiv: https://arxiv.org/abs/1610.02579
• github: https://github.com/craftGBD/craftGBD

StuffNet: Using ‘Stuff’ to Improve Object Detection

• arxiv: https://arxiv.org/abs/1610.05861

Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

• arxiv: https://arxiv.org/abs/1610.09609

Hierarchical Object Detection with Deep Reinforcement Learning

• intro: Deep Reinforcement Learning Workshop (NIPS 2016)
• project page: https://imatge-upc.github.io/detection-2016-nipsws/
• arxiv: https://arxiv.org/abs/1611.03718
• slides: http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning
• github: https://github.com/imatge-upc/detection-2016-nipsws
• blog: http://jorditorres.org/nips/

Learning to detect and localize many objects from few examples

• arxiv: https://arxiv.org/abs/1611.05664

Speed/accuracy trade-offs for modern convolutional object detectors

• intro: CVPR 2017. Google Research
• arxiv: https://arxiv.org/abs/1611.10012

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

• arxiv: https://arxiv.org/abs/1612.01051
• github: https://github.com/BichenWuUCB/squeezeDet
• github: https://github.com/fregu856/2D_detection

---

Feature Pyramid Network (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

• arxiv: 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

• arxiv: https://arxiv.org/abs/1706.05274

Few-shot Object Detection

• arxiv: https://arxiv.org/abs/1706.08249

Yes-Net: An effective Detector Based on Global Information

• arxiv: https://arxiv.org/abs/1706.09180

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

• arxiv: https://arxiv.org/abs/1706.10217

Towards lightweight convolutional neural networks for object detection

• arxiv: 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

Object Detection from Scratch with Deep Supervision

• 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

Focal Loss Dense Detector for Vehicle Surveillance

• arxiv: https://arxiv.org/abs/1803.01114

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

• arxiv: https://arxiv.org/abs/1709.04347

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

• arxiv: 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

Single-Shot Refinement Neural Network for Object Detection

• arxiv: https://arxiv.org/abs/1711.06897
• github: https://github.com/sfzhang15/RefineDet
• github: https://github.com/MTCloudVision/RefineDet-Mxnet

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

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1711.08189
• github: https://github.com/bharatsingh430/snip

Feature Selective Networks for Object Detection

• arxiv: https://arxiv.org/abs/1711.08879

Learning a Rotation Invariant Detector with Rotatable Bounding Box

• arxiv: https://arxiv.org/abs/1711.09405
• github(official, Caffe): 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

Domain Adaptive Faster R-CNN for Object Detection in the Wild

• intro: CVPR 2018. ETH Zurich & ESAT/PSI
• arxiv: https://arxiv.org/abs/1803.03243
• github(official. Caffe): https://github.com/yuhuayc/da-faster-rcnn

Pseudo Mask Augmented Object Detection

• arxiv: https://arxiv.org/abs/1803.05858

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

• intro: ECCV 2018
• keywords: DCR V1
• arxiv: https://arxiv.org/abs/1803.06799
• github(official, MXNet): https://github.com/bowenc0221/Decoupled-Classification-Refinement

Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection

• keywords: DCR V2
• arxiv: https://arxiv.org/abs/1810.04002
• github(official, MXNet): https://github.com/bowenc0221/Decoupled-Classification-Refinement

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

DetNet: A Backbone network for Object Detection

• intro: Tsinghua University & Megvii Inc
• arxiv: https://arxiv.org/abs/1804.06215

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

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

AdvDetPatch: Attacking Object Detectors with Adversarial Patches

• arxiv: https://arxiv.org/abs/1806.02299

Attacking Object Detectors via Imperceptible Patches on Background

• https://arxiv.org/abs/1809.05966

Physical Adversarial Examples for Object Detectors

• intro: WOOT 2018
• arxiv: https://arxiv.org/abs/1807.07769

Quantization Mimic: Towards Very Tiny CNN for Object Detection

• arxiv: https://arxiv.org/abs/1805.02152

Object detection at 200 Frames Per Second

• intro: United Technologies Research Center-Ireland
• arxiv: https://arxiv.org/abs/1805.06361

Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images

• intro: CVPR 2018 Deep Vision Workshop
• arxiv: https://arxiv.org/abs/1805.11778

SNIPER: Efficient Multi-Scale Training

• intro: University of Maryland
• keywords: SNIPER (Scale Normalization for Image Pyramid with Efficient Resampling)
• arxiv: https://arxiv.org/abs/1805.09300
• github: https://github.com/mahyarnajibi/SNIPER

Soft Sampling for Robust Object Detection

• arxiv: https://arxiv.org/abs/1806.06986

MetaAnchor: Learning to Detect Objects with Customized Anchors

• intro: Megvii Inc (Face++) & Fudan University
• arxiv: https://arxiv.org/abs/1807.00980

Localization Recall Precision (LRP): A New Performance Metric for Object Detection

• intro: ECCV 2018. Middle East Technical University
• arxiv: https://arxiv.org/abs/1807.01696
• github: https://github.com/cancam/LRP

Auto-Context R-CNN

• intro: Rejected by ECCV18
• arxiv: https://arxiv.org/abs/1807.02842

Pooling Pyramid Network for Object Detection

• intro: Google AI Perception
• arxiv: https://arxiv.org/abs/1807.03284

Modeling Visual Context is Key to Augmenting Object Detection Datasets

• intro: ECCV 2018
• arxiv: https://arxiv.org/abs/1807.07428

Dual Refinement Network for Single-Shot Object Detection

• arxiv: https://arxiv.org/abs/1807.08638

Acquisition of Localization Confidence for Accurate Object Detection

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

CornerNet: Detecting Objects as Paired Keypoints

• intro: ECCV 2018
• keywords: IoU-Net, PreciseRoIPooling
• arxiv: https://arxiv.org/abs/1808.01244
• github: https://github.com/umich-vl/CornerNet

Unsupervised Hard Example Mining from Videos for Improved Object Detection

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

SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection

• arxiv: https://arxiv.org/abs/1808.04974

A Survey of Modern Object Detection Literature using Deep Learning

• arxiv: https://arxiv.org/abs/1808.07256

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

• intro: BMVC 2018
• arxiv: https://arxiv.org/abs/1807.11013
• github: https://github.com/lyxok1/Tiny-DSOD

Deep Feature Pyramid Reconfiguration for Object Detection

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

MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection

• intro: ICPR 2018
• arxiv: https://arxiv.org/abs/1809.01791

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

• https://arxiv.org/abs/1809.03193

Deep Learning for Generic Object Detection: A Survey

• https://arxiv.org/abs/1809.02165

Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples

• intro: ICLR 2018
• arxiv: https://github.com/alinlab/Confident_classifier

ScratchDet:Exploring to Train Single-Shot Object Detectors from Scratch

• arxiv: https://arxiv.org/abs/1810.08425
• github: https://github.com/KimSoybean/ScratchDet

Fast and accurate object detection in high resolution 4K and 8K video using GPUs

• intro: Best Paper Finalist at IEEE High Performance Extreme Computing Conference (HPEC) 2018
• intro: Carnegie Mellon University
• arxiv: https://arxiv.org/abs/1810.10551

Hybrid Knowledge Routed Modules for Large-scale Object Detection

• intro: NIPS 2018
• arxiv: https://arxiv.org/abs/1810.12681
• github(official, PyTorch): https://github.com/chanyn/HKRM

Gradient Harmonized Single-stage Detector

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

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

BAN: Focusing on Boundary Context for Object Detection

• arxiv：https://arxiv.org/abs/1811.05243

Multi-layer Pruning Framework for Compressing Single Shot MultiBox Detector

• intro: WACV 2019
• arxiv: https://arxiv.org/abs/1811.08342

R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy

• arxiv: https://arxiv.org/abs/1811.07126
• github: https://github.com/DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow

DeRPN: Taking a further step toward more general object detection

• intro: AAAI 2019
• intro: South China University of Technology
• arxiv: https://arxiv.org/abs/1811.06700
• github: https://github.com/HCIILAB/DeRPN

Fast Efficient Object Detection Using Selective Attention

• arxiv：https://arxiv.org/abs/1811.07502

Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects

• arxiv：https://arxiv.org/abs/1811.10862

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

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

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

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

Grid R-CNN

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

Transferable Adversarial Attacks for Image and Video Object Detection

-arxiv：https://arxiv.org/abs/1811.12641

Anchor Box Optimization for Object Detection

• intro: University of Illinois at Urbana-Champaign & Microsoft Research
• arxiv: https://arxiv.org/abs/1812.00469

AutoFocus: Efficient Multi-Scale Inference

• intro: University of Maryland
• arxiv: https://arxiv.org/abs/1812.01600

###Few-shot Object Detection via Feature Reweighting

• arxiv：https://arxiv.org/abs/1812.01866

Practical Adversarial Attack Against Object Detector

• arxiv：https://arxiv.org/abs/1812.10217

Learning Efficient Detector with Semi-supervised Adaptive Distillation

• intro: SenseTime Research
• arxiv: https://arxiv.org/abs/1901.00366
• github: https://github.com/Tangshitao/Semi-supervised-Adaptive-Distillation

Scale-Aware Trident Networks for Object Detection

intro: University of Chinese Academy of Sciences & TuSimple
arxiv: https://arxiv.org/abs/1901.01892
github: https://github.com/TuSimple/simpledet

Region Proposal by Guided Anchoring

• intro: CUHK - SenseTime Joint Lab & Amazon Rekognition & Nanyang Technological University
• arxiv: https://arxiv.org/abs/1901.03278

Consistent Optimization for Single-Shot Object Detection

• arxiv: https://arxiv.org/abs/1901.06563
• blog: https://zhuanlan.zhihu.com/p/55416312

Bottom-up Object Detection by Grouping Extreme and Center Points

• keywords: ExtremeNet
• arxiv: https://arxiv.org/abs/1901.08043
• github: https://github.com/xingyizhou/ExtremeNet

A Single-shot Object Detector with Feature Aggragation and Enhancement

• arxiv: https://arxiv.org/abs/1902.02923

Bag of Freebies for Training Object Detection Neural Networks

• intro: Amazon Web Services
• arxiv: https://arxiv.org/abs/1902.04103

---

Non-Maximum Suppression (NMS)

End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression

• intro: CVPR 2015
• arxiv: http://arxiv.org/abs/1411.5309
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wan_End-to-End_Integration_of_2015_CVPR_paper.pdf

A convnet for non-maximum suppression

• arxiv: http://arxiv.org/abs/1511.06437
  Improving Object Detection With One Line of Code

Soft-NMS – Improving Object Detection With One Line of Code

• intro: ICCV 2017. University of Maryland
• keywords: Soft-NMS
• arxiv: https://arxiv.org/abs/1704.04503
• github: https://github.com/bharatsingh430/soft-nms

Learning non-maximum suppression

• intro: CVPR 2017
• project page: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/learning-nms/
• arxiv: https://arxiv.org/abs/1705.02950
• github: https://github.com/hosang/gossipnet

Relation Networks for Object Detection

• intro: CVPR 2018 oral
• arxiv: https://arxiv.org/abs/1711.11575
• github(official, MXNet): https://github.com/msracver/Relation-Networks-for-Object-Detection

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

• keywords: Pairwise-NMS
• arxiv: https://arxiv.org/abs/1901.03796

Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples

• arxiv: https://arxiv.org/abs/1902.02067

---

Adversarial Examples

Adversarial Examples that Fool Detectors

• intro: University of Illinois
• arxiv: https://arxiv.org/abs/1712.02494

Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

• project page: http://nicholas.carlini.com/code/nn_breaking_detection/
• arxiv: https://arxiv.org/abs/1705.07263
• github: https://github.com/carlini/nn_breaking_detection

---

Weakly Supervised Object Detection

Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection

• intro: CVPR 2016
• arxiv: http://arxiv.org/abs/1604.05766

Weakly supervised object detection using pseudo-strong labels

• arxiv: http://arxiv.org/abs/1607.04731

Saliency Guided End-to-End Learning for Weakly Supervised Object Detection

• intro: IJCAI 2017
• arxiv: https://arxiv.org/abs/1706.06768

Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

• intro: TPAMI 2017. National Institutes of Health (NIH) Clinical Center
• arxiv: https://arxiv.org/abs/1801.03145

---

Video Object Detection

Learning Object Class Detectors from Weakly Annotated Video

• intro: CVPR 2012
• paper: https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_00905.pdf

Analysing domain shift factors between videos and images for object detection

• arxiv: https://arxiv.org/abs/1501.01186

Video Object Recognition

• slides: http://vision.princeton.edu/courses/COS598/2015sp/slides/VideoRecog/Video%20Object%20Recognition.pptx

Deep Learning for Saliency Prediction in Natural Video

• intro: Submitted on 12 Jan 2016
• keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
• paper: https://hal.archives-ouvertes.fr/hal-01251614/document

T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

• intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
• arxiv: http://arxiv.org/abs/1604.02532
• github: https://github.com/myfavouritekk/T-CNN

Object Detection from Video Tubelets with Convolutional Neural Networks

• intro: CVPR 2016 Spotlight paper
• arxiv: https://arxiv.org/abs/1604.04053
• paper: http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf
• gihtub: https://github.com/myfavouritekk/vdetlib

Object Detection in Videos with Tubelets and Multi-context Cues

• intro: SenseTime Group
• slides: http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf
• slides: http://image-net.org/challenges/talks/Object%20Detection%20in%20Videos%20with%20Tubelets%20and%20Multi-context%20Cues%20-%20Final.pdf

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks

• intro: BMVC 2016
• keywords: pseudo-labeler
• arxiv: http://arxiv.org/abs/1607.04648
• paper: http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf

CNN Based Object Detection in Large Video Images

• intro: WangTao @ 爱奇艺
• keywords: object retrieval, object detection, scene classification
• slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6362-wang-tao-cnn-based-object-detection-large-video-images.pdf

Object Detection in Videos with Tubelet Proposal Networks

• arxiv: https://arxiv.org/abs/1702.06355

Flow-Guided Feature Aggregation for Video Object Detection

• intro: MSRA
• arxiv: https://arxiv.org/abs/1703.10025

Video Object Detection using Faster R-CNN

• blog: http://andrewliao11.github.io/object_detection/faster_rcnn/
• github: https://github.com/andrewliao11/py-faster-rcnn-imagenet

Improving Context Modeling for Video Object Detection and Tracking

http://image-net.org/challenges/talks_2017/ilsvrc2017_short(poster).pdf

Temporal Dynamic Graph LSTM for Action-driven Video Object Detection

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1708.00666

Mobile Video Object Detection with Temporally-Aware Feature Maps

• arxiv: https://arxiv.org/abs/1711.06368

Towards High Performance Video Object Detection

• arxiv: https://arxiv.org/abs/1711.11577

Impression Network for Video Object Detection

• arxiv: https://arxiv.org/abs/1712.05896

Spatial-Temporal Memory Networks for Video Object Detection

• arxiv: https://arxiv.org/abs/1712.06317

3D-DETNet: a Single Stage Video-Based Vehicle Detector

• arxiv: https://arxiv.org/abs/1801.01769

Object Detection in Videos by Short and Long Range Object Linking

• arxiv: https://arxiv.org/abs/1801.09823

Object Detection in Video with Spatiotemporal Sampling Networks

• intro: University of Pennsylvania, 2Dartmouth College
• arxiv: https://arxiv.org/abs/1803.05549

Towards High Performance Video Object Detection for Mobiles

• intro: Microsoft Research Asia
• arxiv: https://arxiv.org/abs/1804.05830

Optimizing Video Object Detection via a Scale-Time Lattice

• intro: CVPR 2018
• project page: http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice/
• arxiv: https://arxiv.org/abs/1804.05472
• github: https://github.com/hellock/scale-time-lattice

Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing

• https://arxiv.org/abs/1809.01701

Fast Object Detection in Compressed Video

• arxiv：https://arxiv.org/abs/1811.11057

Tube-CNN: Modeling temporal evolution of appearance for object detection in video

• intro: INRIA/ENS
• arxiv: https://arxiv.org/abs/1812.02619

AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling

• intro: SysML 2019 oral
• arxiv: https://arxiv.org/abs/1902.02910

---

Object Detection on Mobile Devices

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

• intro: ICLR 2018 workshop track
• intro: based on the SSD
• arxiv: https://arxiv.org/abs/1804.06882
• github: https://github.com/Robert-JunWang/Pelee

---

Object Detection in 3D

Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

• arxiv: https://arxiv.org/abs/1609.06666

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

• intro: Valeo Schalter und Sensoren GmbH & Ilmenau University of Technology
• arxiv: https://arxiv.org/abs/1803.06199

Focal Loss in 3D Object Detection

• arxiv: https://arxiv.org/abs/1809.06065
• github: https://github.com/pyun-ram/FL3D

3D Object Detection Using Scale Invariant and Feature Reweighting Networks

• intro: AAAI 2019
• arxiv: https://arxiv.org/abs/1901.02237

3D Backbone Network for 3D Object Detection

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

---

Object Detection on RGB-D

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

• arxiv: http://arxiv.org/abs/1407.5736

Differential Geometry Boosts Convolutional Neural Networks for Object Detection

• intro: CVPR 2016
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w23/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation

• arxiv: https://arxiv.org/abs/1703.03347

---

Zero-Shot Object Detection

Zero-Shot Detection

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

Zero-Shot Object Detection

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

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

• intro: Australian National University
• arxiv: https://arxiv.org/abs/1803.06049

Zero-Shot Object Detection by Hybrid Region Embedding

• intro: Middle East Technical University & Hacettepe University
• arxiv: https://arxiv.org/abs/1805.06157

---

Salient Object Detection

This task involves predicting the salient regions of an image given by human eye fixations.

Best Deep Saliency Detection Models (CVPR 2016 & 2015)

• page: http://i.cs.hku.hk/~yzyu/vision.html

Large-scale optimization of hierarchical features for saliency prediction in natural images

• paper: http://coxlab.org/pdfs/cvpr2014_vig_saliency.pdf

Predicting Eye Fixations using Convolutional Neural Networks

• paper: http://www.escience.cn/system/file?fileId=72648

Saliency Detection by Multi-Context Deep Learning

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhao_Saliency_Detection_by_2015_CVPR_paper.pdf

DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

• arxiv: http://arxiv.org/abs/1510.05484

SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

• paper: www.shengfenghe.com/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html

Shallow and Deep Convolutional Networks for Saliency Prediction

• intro: CVPR 2016
• arxiv: http://arxiv.org/abs/1603.00845
• github: https://github.com/imatge-upc/saliency-2016-cvpr

Recurrent Attentional Networks for Saliency Detection

• intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN)
• arxiv: http://arxiv.org/abs/1604.03227

Two-Stream Convolutional Networks for Dynamic Saliency Prediction

• arxiv: http://arxiv.org/abs/1607.04730

---

Unconstrained Salient Object Detection

Unconstrained Salient Object Detection via Proposal Subset Optimization

• intro: CVPR 2016
• project page: http://cs-people.bu.edu/jmzhang/sod.html
• paper: http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD_camera_ready.pdf
• github: https://github.com/jimmie33/SOD
• caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-object-proposal-models-for-salient-object-detection

DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf

Salient Object Subitizing

• intro: CVPR 2015
• intro: predicting the existence and the number of salient objects in an image using holistic cues
• project page: http://cs-people.bu.edu/jmzhang/sos.html
• arxiv: http://arxiv.org/abs/1607.07525
• paper: http://cs-people.bu.edu/jmzhang/SOS/SOS_preprint.pdf
• caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-models-for-salient-object-subitizing

Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

• intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN)
• arxiv: http://arxiv.org/abs/1608.05177

Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1608.05186

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

• arxiv: http://arxiv.org/abs/1608.08029

A Deep Multi-Level Network for Saliency Prediction

• arxiv: http://arxiv.org/abs/1609.01064

Visual Saliency Detection Based on Multiscale Deep CNN Features

• intro: IEEE Transactions on Image Processing
• arxiv: http://arxiv.org/abs/1609.02077

A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection

• intro: DSCLRCN
• arxiv: https://arxiv.org/abs/1610.01708

Deeply supervised salient object detection with short connections

• intro: IEEE TPAMI 2018 (IEEE CVPR 2017)
• arxiv: https://arxiv.org/abs/1611.04849
• github(official, Caffe): https://github.com/Andrew-Qibin/DSS
• github(Tensorflow): https://github.com/Joker316701882/Salient-Object-Detection

Weakly Supervised Top-down Salient Object Detection

• intro: Nanyang Technological University
• arxiv: https://arxiv.org/abs/1611.05345

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

• project page: https://imatge-upc.github.io/saliency-salgan-2017/
• arxiv: https://arxiv.org/abs/1701.01081

Visual Saliency Prediction Using a Mixture of Deep Neural Networks

• arxiv: https://arxiv.org/abs/1702.00372

A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network

• arxiv: https://arxiv.org/abs/1702.00615

Saliency Detection by Forward and Backward Cues in Deep-CNNs

• arxiv: https://arxiv.org/abs/1703.00152

Supervised Adversarial Networks for Image Saliency Detection

• arxiv: https://arxiv.org/abs/1704.07242

Group-wise Deep Co-saliency Detection

• arxiv: https://arxiv.org/abs/1707.07381

Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection

• intro: University of Maryland College Park & eBay Inc
• arxiv: https://arxiv.org/abs/1708.00079

Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection

• intro: ICCV 2017
• arixv: https://arxiv.org/abs/1708.02001

Learning Uncertain Convolutional Features for Accurate Saliency Detection

• intro: Accepted as a poster in ICCV 2017
• arxiv: https://arxiv.org/abs/1708.02031

Deep Edge-Aware Saliency Detection

• arxiv: https://arxiv.org/abs/1708.04366

Self-explanatory Deep Salient Object Detection

• intro: National University of Defense Technology, China & National University of Singapore
• arxiv: https://arxiv.org/abs/1708.05595

PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection

• arxiv: https://arxiv.org/abs/1708.06433

DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets

• arxiv: https://arxiv.org/abs/1709.02495

Recurrently Aggregating Deep Features for Salient Object Detection

• intro: AAAI 2018
• paper: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16775/16281

Deep saliency: What is learnt by a deep network about saliency?

• intro: 2nd Workshop on Visualisation for Deep Learning in the 34th International Conference On Machine Learning
• arxiv: https://arxiv.org/abs/1801.04261

Contrast-Oriented Deep Neural Networks for Salient Object Detection

• intro: TNNLS
• arxiv: https://arxiv.org/abs/1803.11395

Salient Object Detection by Lossless Feature Reflection

• intro: IJCAI 2018
• arxiv: https://arxiv.org/abs/1802.06527

HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection

• arxiv: https://arxiv.org/abs/1804.05142

---

Video Saliency Detection

Deep Learning For Video Saliency Detection

• arxiv: https://arxiv.org/abs/1702.00871

Video Salient Object Detection Using Spatiotemporal Deep Features

• arxiv: https://arxiv.org/abs/1708.01447

Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

• arxiv: https://arxiv.org/abs/1709.06316

---

Visual Relationship Detection

Visual Relationship Detection with Language Priors

• intro: ECCV 2016 oral
• paper: https://cs.stanford.edu/people/ranjaykrishna/vrd/vrd.pdf
• github: https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection

ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection

• intro: Visual Phrase reasoning Convolutional Neural Network (ViP-CNN), Visual Phrase Reasoning Structure (VPRS)
• arxiv: https://arxiv.org/abs/1702.07191

Visual Translation Embedding Network for Visual Relation Detection

• arxiv: https://www.arxiv.org/abs/1702.08319

Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection

• intro: CVPR 2017 spotlight paper
• arxiv: https://arxiv.org/abs/1703.03054

Detecting Visual Relationships with Deep Relational Networks

• intro: CVPR 2017 oral. The Chinese University of Hong Kong
• arxiv: https://arxiv.org/abs/1704.03114

Identifying Spatial Relations in Images using Convolutional Neural Networks

• arxiv: https://arxiv.org/abs/1706.04215

PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN

• intro: ICCV
• arxiv: https://arxiv.org/abs/1708.01956

Natural Language Guided Visual Relationship Detection

• arxiv: https://arxiv.org/abs/1711.06032

Detecting Visual Relationships Using Box Attention

• intro: Google AI & IST Austria
• arxiv: https://arxiv.org/abs/1807.02136

Google AI Open Images - Visual Relationship Track

• intro: Detect pairs of objects in particular relationships
• kaggle: https://www.kaggle.com/c/google-ai-open-images-visual-relationship-track

Context-Dependent Diffusion Network for Visual Relationship Detection

• intro: 2018 ACM Multimedia Conference
• arxiv: https://arxiv.org/abs/1809.06213

A Problem Reduction Approach for Visual Relationships Detection

• intro: ECCV 2018 Workshop
• arxiv: https://arxiv.org/abs/1809.09828

---

Face Deteciton

Multi-view Face Detection Using Deep Convolutional Neural Networks

• intro: Yahoo
• arxiv: http://arxiv.org/abs/1502.02766
• github: https://github.com/guoyilin/FaceDetection_CNN

From Facial Parts Responses to Face Detection: A Deep Learning Approach

• intro: ICCV 2015. CUHK
• project page: http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html
• arxiv: https://arxiv.org/abs/1509.06451
• paper: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yang_From_Facial_Parts_ICCV_2015_paper.pdf

Compact Convolutional Neural Network Cascade for Face Detection

• arxiv: http://arxiv.org/abs/1508.01292
• github: https://github.com/Bkmz21/FD-Evaluation
• github: https://github.com/Bkmz21/CompactCNNCascade

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1606.00850
• github(MXNet): https://github.com/tfwu/FaceDetection-ConvNet-3D

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

• intro: CMU
• arxiv: https://arxiv.org/abs/1606.05413

Towards a Deep Learning Framework for Unconstrained Face Detection

• intro: overlap with CMS-RCNN
• arxiv: https://arxiv.org/abs/1612.05322

Supervised Transformer Network for Efficient Face Detection

• arxiv: http://arxiv.org/abs/1607.05477

UnitBox: An Advanced Object Detection Network

• intro: ACM MM 2016
• keywords: IOULoss
• arxiv: http://arxiv.org/abs/1608.01471

Bootstrapping Face Detection with Hard Negative Examples

• author: 万韶华 @ 小米.
• intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset
• arxiv: http://arxiv.org/abs/1608.02236

Grid Loss: Detecting Occluded Faces

• intro: ECCV 2016
• arxiv: https://arxiv.org/abs/1609.00129
• paper: http://lrs.icg.tugraz.at/pubs/opitz_eccv_16.pdf
• poster: http://www.eccv2016.org/files/posters/P-2A-34.pdf

A Multi-Scale Cascade Fully Convolutional Network Face Detector

• intro: ICPR 2016
• arxiv: http://arxiv.org/abs/1609.03536

---

MTCNN

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

• project page: https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html
• arxiv: https://arxiv.org/abs/1604.02878
• github(official, Matlab): https://github.com/kpzhang93/MTCNN_face_detection_alignment
• github: https://github.com/pangyupo/mxnet_mtcnn_face_detection
• github: https://github.com/DaFuCoding/MTCNN_Caffe
• github(MXNet): https://github.com/Seanlinx/mtcnn
• github: https://github.com/Pi-DeepLearning/RaspberryPi-FaceDetection-MTCNN-Caffe-With-Motion
• github(Caffe): https://github.com/foreverYoungGitHub/MTCNN
• github: https://github.com/CongWeilin/mtcnn-caffe
• github(OpenCV+OpenBlas): https://github.com/AlphaQi/MTCNN-light
• github(Tensorflow+golang): https://github.com/jdeng/goface

Face Detection using Deep Learning: An Improved Faster RCNN Approach

• intro: DeepIR Inc
• arxiv: https://arxiv.org/abs/1701.08289

Faceness-Net: Face Detection through Deep Facial Part Responses

• intro: An extended version of ICCV 2015 paper
• arxiv: https://arxiv.org/abs/1701.08393

Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”

• intro: CVPR 2017. MP-RCNN, MP-RPN
• arxiv: https://arxiv.org/abs/1703.09145

End-To-End Face Detection and Recognition

• arxiv: https://arxiv.org/abs/1703.10818

Face R-CNN

• arxiv: https://arxiv.org/abs/1706.01061

Face Detection through Scale-Friendly Deep Convolutional Networks

• arxiv: https://arxiv.org/abs/1706.02863

Scale-Aware Face Detection

• intro: CVPR 2017. SenseTime & Tsinghua University
• arxiv: https://arxiv.org/abs/1706.09876

Detecting Faces Using Inside Cascaded Contextual CNN

• intro: CVPR 2017. Tencent AI Lab & SenseTime
• paper: http://ai.tencent.com/ailab/media/publications/Detecting_Faces_Using_Inside_Cascaded_Contextual_CNN.pdf

Multi-Branch Fully Convolutional Network for Face Detection

• arxiv: https://arxiv.org/abs/1707.06330

SSH: Single Stage Headless Face Detector

• intro: ICCV 2017. University of Maryland
• arxiv: https://arxiv.org/abs/1708.03979
• github(official, Caffe): https://github.com/mahyarnajibi/SSH

Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container

• arxiv: https://arxiv.org/abs/1708.04370

FaceBoxes: A CPU Real-time Face Detector with High Accuracy

• intro: IJCB 2017
• keywords: Rapidly Digested Convolutional Layers (RDCL), Multiple Scale Convolutional Layers (MSCL)
• intro: the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images
• arxiv: https://arxiv.org/abs/1708.05234
• github(official): https://github.com/sfzhang15/FaceBoxes
• github(Caffe): https://github.com/zeusees/FaceBoxes

S3FD: Single Shot Scale-invariant Face Detector

• intro: ICCV 2017. Chinese Academy of Sciences
• intro: can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images
• arxiv: https://arxiv.org/abs/1708.05237
• github(Caffe, official): https://github.com/sfzhang15/SFD
• github: https://github.com//clcarwin/SFD_pytorch

Detecting Faces Using Region-based Fully Convolutional Networks

• arxiv: https://arxiv.org/abs/1709.05256

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

• arxiv: https://arxiv.org/abs/1709.07326

Face Attention Network: An effective Face Detector for the Occluded Faces

• arxiv: https://arxiv.org/abs/1711.07246

Feature Agglomeration Networks for Single Stage Face Detection

• arxiv: https://arxiv.org/abs/1712.00721

Face Detection Using Improved Faster RCNN

• intro: Huawei Cloud BU
• arxiv: https://arxiv.org/abs/1802.02142

PyramidBox: A Context-assisted Single Shot Face Detector

• intro: Baidu, Inc
• arxiv: https://arxiv.org/abs/1803.07737

A Fast Face Detection Method via Convolutional Neural Network

• intro: Neurocomputing
• arxiv: https://arxiv.org/abs/1803.10103

Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy

• intro: CVPR 2018. Beihang University & CUHK & Sensetime
• arxiv: https://arxiv.org/abs/1804.05197

Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1804.06039
• github: https://github.com/Jack-CV/PCN

SFace: An Efficient Network for Face Detection in Large Scale Variations

• intro: Beihang University & Megvii Inc. (Face++)
• arxiv: https://arxiv.org/abs/1804.06559

Survey of Face Detection on Low-quality Images

• arxiv: https://arxiv.org/abs/1804.07362

Anchor Cascade for Efficient Face Detection

• intro: The University of Sydney
• arxiv: https://arxiv.org/abs/1805.03363

Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization

• intro: IEEE MMSP
• arxiv: https://arxiv.org/abs/1805.12302

Selective Refinement Network for High Performance Face Detection

• https://arxiv.org/abs/1809.02693

DSFD: Dual Shot Face Detector

• arxiv：https://arxiv.org/abs/1810.10220

Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision

• arxiv：https://arxiv.org/abs/1811.08557

FA-RPN: Floating Region Proposals for Face Detection

• arxiv: https://arxiv.org/abs/1812.05586

Robust and High Performance Face Detector

https://arxiv.org/abs/1901.02350

DAFE-FD: Density Aware Feature Enrichment for Face Detection

• arxiv: https://arxiv.org/abs/1901.05375

Improved Selective Refinement Network for Face Detection

• intro: Chinese Academy of Sciences & JD AI Research
• arxiv: https://arxiv.org/abs/1901.06651

Revisiting a single-stage method for face detection

• arxiv: https://arxiv.org/abs/1902.01559

---

Detect Small Faces

Finding Tiny Faces

• intro: CVPR 2017. CMU
• project page: http://www.cs.cmu.edu/~peiyunh/tiny/index.html
• arxiv: https://arxiv.org/abs/1612.04402
• github(official, Matlab): https://github.com/peiyunh/tiny
• github(inference-only): https://github.com/chinakook/hr101_mxnet
• github: https://github.com/cydonia999/Tiny_Faces_in_Tensorflow

Detecting and counting tiny faces

• intro: ENS Paris-Saclay. ExtendedTinyFaces
• intro: Detecting and counting small objects - Analysis, review and application to counting
• arxiv: https://arxiv.org/abs/1801.06504
• github: https://github.com/alexattia/ExtendedTinyFaces

Seeing Small Faces from Robust Anchor’s Perspective

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1802.09058

Face-MagNet: Magnifying Feature Maps to Detect Small Faces

• intro: WACV 2018
• keywords: Face Magnifier Network (Face-MageNet)
• arxiv: https://arxiv.org/abs/1803.05258
• github: https://github.com/po0ya/face-magnet

Robust Face Detection via Learning Small Faces on Hard Images

• intro: Johns Hopkins University & Stanford University
• arxiv: https://arxiv.org/abs/1811.11662
• github: https://github.com/bairdzhang/smallhardface

SFA: Small Faces Attention Face Detector

• intro: Jilin University
• arxiv: https://arxiv.org/abs/1812.08402

---

Person Head Detection

Context-aware CNNs for person head detection

• intro: ICCV 2015
• project page: http://www.di.ens.fr/willow/research/headdetection/
• arxiv: http://arxiv.org/abs/1511.07917
• github: https://github.com/aosokin/cnn_head_detection

Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture

• arxiv: https://arxiv.org/abs/1803.09256

A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications

• https://arxiv.org/abs/1809.03336

FCHD: A fast and accurate head detector

• arxiv: https://arxiv.org/abs/1809.08766
• github(PyTorch, official): https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector

---

Pedestrian Detection / People Detection

Pedestrian Detection aided by Deep Learning Semantic Tasks

• intro: CVPR 2015
• project page: http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/
• arxiv: http://arxiv.org/abs/1412.0069

Deep Learning Strong Parts for Pedestrian Detection

• intro: ICCV 2015. CUHK. DeepParts
• intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset
• paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/tianLWTiccv15.pdf

Taking a Deeper Look at Pedestrians

• intro: CVPR 2015
• arxiv: https://arxiv.org/abs/1501.05790

Convolutional Channel Features

• intro: ICCV 2015
• arxiv: https://arxiv.org/abs/1504.07339
• github: https://github.com/byangderek/CCF

End-to-end people detection in crowded scenes

• arxiv: http://arxiv.org/abs/1506.04878
• github: https://github.com/Russell91/reinspect
• ipn: http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation_reinspect.ipynb
• youtube: https://www.youtube.com/watch?v=QeWl0h3kQ24

Learning Complexity-Aware Cascades for Deep Pedestrian Detection

• intro: ICCV 2015
• arxiv: https://arxiv.org/abs/1507.05348

Deep convolutional neural networks for pedestrian detection

• arxiv: http://arxiv.org/abs/1510.03608
• github: https://github.com/DenisTome/DeepPed

Scale-aware Fast R-CNN for Pedestrian Detection

• arxiv: https://arxiv.org/abs/1510.08160

New algorithm improves speed and accuracy of pedestrian detection

• blog: http://www.eurekalert.org/pub_releases/2016-02/uoc–nai020516.php

Pushing the Limits of Deep CNNs for Pedestrian Detection

• intro: “set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from 11.7% to 8.9%”
• arxiv: http://arxiv.org/abs/1603.04525

A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

• arxiv: http://arxiv.org/abs/1607.04436

A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation

• arxiv: http://arxiv.org/abs/1607.04441

Is Faster R-CNN Doing Well for Pedestrian Detection?

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1607.07032
• github: https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian

Unsupervised Deep Domain Adaptation for Pedestrian Detection

• intro: ECCV Workshop 2016
• arxiv: https://arxiv.org/abs/1802.03269

Reduced Memory Region Based Deep Convolutional Neural Network Detection

• intro: IEEE 2016 ICCE-Berlin
• arxiv: http://arxiv.org/abs/1609.02500

Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection

• arxiv: https://arxiv.org/abs/1610.03466

Detecting People in Artwork with CNNs

• intro: ECCV 2016 Workshops
• arxiv: https://arxiv.org/abs/1610.08871

Multispectral Deep Neural Networks for Pedestrian Detection

• intro: BMVC 2016 oral
• arxiv: https://arxiv.org/abs/1611.02644

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

• arxiv: https://arxiv.org/abs/1902.05291

Deep Multi-camera People Detection

• arxiv: https://arxiv.org/abs/1702.04593

Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters

• intro: CVPR 2017
• project page: http://ml.cs.tsinghua.edu.cn:5000/publications/synunity/
• arxiv: https://arxiv.org/abs/1703.06283
• github(Tensorflow): https://github.com/huangshiyu13/RPNplus

What Can Help Pedestrian Detection?

• intro: CVPR 2017. Tsinghua University & Peking University & Megvii Inc.
• keywords: Faster R-CNN, HyperLearner
• arxiv: https://arxiv.org/abs/1705.02757
• paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Mao_What_Can_Help_CVPR_2017_paper.pdf

Illuminating Pedestrians via Simultaneous Detection & Segmentation

• arxiv: https://arxiv.org/abs/1706.08564

Rotational Rectification Network for Robust Pedestrian Detection

• intro: CMU & Volvo Construction
• arxiv: https://arxiv.org/abs/1706.08917

STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos

• intro: The University of North Carolina at Chapel Hill
• arxiv: https://arxiv.org/abs/1707.09100

Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy

• arxiv: https://arxiv.org/abs/1709.00235

Repulsion Loss: Detecting Pedestrians in a Crowd

• arxiv: https://arxiv.org/abs/1711.07752

Aggregated Channels Network for Real-Time Pedestrian Detection

• arxiv: https://arxiv.org/abs/1801.00476

Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection

• intro: State Key Lab of CAD&CG, Zhejiang University
• arxiv: https://arxiv.org/abs/1803.05347

Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection

• arxiv: https://arxiv.org/abs/1804.00872

Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

• arxiv: https://arxiv.org/abs/1804.02047

PCN: Part and Context Information for Pedestrian Detection with CNNs

• intro: British Machine Vision Conference(BMVC) 2017
• arxiv: https://arxiv.org/abs/1804.04483

Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation

• intro: ECCV 2018. Hikvision Research Institute
• arxiv: https://arxiv.org/abs/1807.01438

Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

• intro: ECCV 2018
• arxiv: https://arxiv.org/abs/1807.08407

Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation

• intro: BMVC 2018
• arxiv: https://arxiv.org/abs/1808.04818

Pedestrian Detection with Autoregressive Network Phases

• intro: Michigan State University
• arxiv: https://arxiv.org/abs/1812.00440

The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection

-arxiv: https://arxiv.org/abs/1901.02645

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Vehicle Detection

DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1607.04564

Evolving Boxes for fast Vehicle Detection

• arxiv: https://arxiv.org/abs/1702.00254

Fine-Grained Car Detection for Visual Census Estimation

• intro: AAAI 2016
• arxiv: https://arxiv.org/abs/1709.02480

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

• intro: IEEE Transactions on Intelligent Transportation Systems (T-ITS)
• arxiv: https://arxiv.org/abs/1804.00433

Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data

• intro: UC Berkeley
• arxiv: https://arxiv.org/abs/1808.08603

Domain Randomization for Scene-Specific Car Detection and Pose Estimation

• arxiv：https://arxiv.org/abs/1811.05939

ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery

• intro: ECCV 2018, UAVision 2018
• arxiv: https://arxiv.org/abs/1811.06318

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Traffic-Sign Detection

Traffic-Sign Detection and Classification in the Wild

• intro: CVPR 2016
• project page(code+dataset): http://cg.cs.tsinghua.edu.cn/traffic-sign/
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf
• code & model: http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/newdata0411.zip

Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data

• intro: CVPR 2017 workshop
• paper: http://openaccess.thecvf.com/content_cvpr_2017_workshops/w9/papers/Jensen_Evaluating_State-Of-The-Art_Object_CVPR_2017_paper.pdf

Detecting Small Signs from Large Images

• intro: IEEE Conference on Information Reuse and Integration (IRI) 2017 oral
• arxiv: https://arxiv.org/abs/1706.08574

Localized Traffic Sign Detection with Multi-scale Deconvolution Networks

• arxiv: https://arxiv.org/abs/1804.10428

Detecting Traffic Lights by Single Shot Detection

• intro: ITSC 2018
• arxiv: https://arxiv.org/abs/1805.02523

A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection

• intro: IEEE 15th Conference on Computer and Robot Vision
• arxiv: https://arxiv.org/abs/1806.07987
• demo: https://www.youtube.com/watch?v=_YmogPzBXOw&feature=youtu.be

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Skeleton Detection

Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

• arxiv: http://arxiv.org/abs/1603.09446
• github: https://github.com/zeakey/DeepSkeleton

DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images

• arxiv: http://arxiv.org/abs/1609.03659

SRN: Side-output Residual Network for Object Symmetry Detection in the Wild

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1703.02243
• github: https://github.com/KevinKecc/SRN

Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

• arxiv: https://arxiv.org/abs/1801.01849

---

Fruit Detection

Deep Fruit Detection in Orchards

• arxiv: https://arxiv.org/abs/1610.03677

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

• intro: The Journal of Field Robotics in May 2016
• project page: http://confluence.acfr.usyd.edu.au/display/AGPub/
• arxiv: https://arxiv.org/abs/1610.08120

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Shadow Detection

Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network

• arxiv: https://arxiv.org/abs/1709.09283

A+D-Net: Shadow Detection with Adversarial Shadow Attenuation

• arxiv: https://arxiv.org/abs/1712.01361

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

• arxiv: https://arxiv.org/abs/1712.02478

Direction-aware Spatial Context Features for Shadow Detection

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1712.04142

Direction-aware Spatial Context Features for Shadow Detection and Removal

• intro: The Chinese University of Hong Kong & The Hong Kong Polytechnic University
• arxiv: https://arxiv.org/abs/1805.04635

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Others Detection

Deep Deformation Network for Object Landmark Localization

• arxiv: http://arxiv.org/abs/1605.01014

Fashion Landmark Detection in the Wild

• intro: ECCV 2016
• project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/FashionLandmarks.html
• arxiv: http://arxiv.org/abs/1608.03049
• github(Caffe): https://github.com/liuziwei7/fashion-landmarks

Deep Learning for Fast and Accurate Fashion Item Detection

• intro: Kuznech Inc.
• intro: MultiBox and Fast R-CNN
• paper: https://kddfashion2016.mybluemix.net/kddfashion_finalSubmissions/Deep%20Learning%20for%20Fast%20and%20Accurate%20Fashion%20Item%20Detection.pdf

OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)

• github: https://github.com/geometalab/OSMDeepOD

Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

• intro: IEEE SITIS 2016
• arxiv: https://arxiv.org/abs/1611.04357

Associative Embedding:End-to-End Learning for Joint Detection and Grouping

• arxiv: https://arxiv.org/abs/1611.05424

Deep Cuboid Detection: Beyond 2D Bounding Boxes

• intro: CMU & Magic Leap
• arxiv: https://arxiv.org/abs/1611.10010

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

• arxiv: https://arxiv.org/abs/1612.03019

Deep Learning Logo Detection with Data Expansion by Synthesising Context

• arxiv: https://arxiv.org/abs/1612.09322

Scalable Deep Learning Logo Detection

• arxiv: https://arxiv.org/abs/1803.11417

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

• arxiv: https://arxiv.org/abs/1702.00307

Automatic Handgun Detection Alarm in Videos Using Deep Learning

• arxiv: https://arxiv.org/abs/1702.05147
• results: https://github.com/SihamTabik/Pistol-Detection-in-Videos

Objects as context for part detection

• arxiv: https://arxiv.org/abs/1703.09529

Using Deep Networks for Drone Detection

• intro: AVSS 2017
• arxiv: https://arxiv.org/abs/1706.05726

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1708.01642

Target Driven Instance Detection

• arxiv: https://arxiv.org/abs/1803.04610

DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion

• arxiv: https://arxiv.org/abs/1709.04577

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1710.06288
• github: https://github.com/SeokjuLee/VPGNet

Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants

• arxiv: https://arxiv.org/abs/1711.05128

ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos

• intro: WACV 2018
• arxiv: https://arxiv.org/abs/1801.02031

Deep Learning Object Detection Methods for Ecological Camera Trap Data

• intro: Conference of Computer and Robot Vision. University of Guelph
• arxiv: https://arxiv.org/abs/1803.10842

EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection

• arxiv: https://arxiv.org/abs/1806.05525

Towards End-to-End Lane Detection: an Instance Segmentation Approach

• arxiv: https://arxiv.org/abs/1802.05591
• github: https://github.com/MaybeShewill-CV/lanenet-lane-detection

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

• intro: BMVC 2018
• project page: https://gaochen315.github.io/iCAN/
• arxiv: https://arxiv.org/abs/1808.10437
• github: https://github.com/vt-vl-lab/iCAN

Densely Supervised Grasp Detector (DSGD)

• https://arxiv.org/abs/1810.03962

---

Object Proposal

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

• arxiv: http://arxiv.org/abs/1510.04445
• github: https://github.com/aghodrati/deepproposal

Scale-aware Pixel-wise Object Proposal Networks

• intro: IEEE Transactions on Image Processing
• arxiv: http://arxiv.org/abs/1601.04798

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

• intro: BMVC 2016. AttractioNet
• arxiv: https://arxiv.org/abs/1606.04446
• github: https://github.com/gidariss/AttractioNet

Learning to Segment Object Proposals via Recursive Neural Networks

• arxiv: https://arxiv.org/abs/1612.01057

Learning Detection with Diverse Proposals

• intro: CVPR 2017
• keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse Proposals (LDDP)
• arxiv: https://arxiv.org/abs/1704.03533

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

• keywords: product detection
• arxiv: https://arxiv.org/abs/1704.06752

Improving Small Object Proposals for Company Logo Detection

• intro: ICMR 2017
• arxiv: https://arxiv.org/abs/1704.08881

Open Logo Detection Challenge

• intro: BMVC 2018
• keywords: QMUL-OpenLogo
• project page: https://qmul-openlogo.github.io/
• arxiv: https://arxiv.org/abs/1807.01964

AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects

• intro: ACCV 2018 oral
• arxiv: https://arxiv.org/abs/1811.08728
• github: https://github.com/chwilms/AttentionMask

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Localization

Beyond Bounding Boxes: Precise Localization of Objects in Images

• intro: PhD Thesis
• homepage: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.html
• phd-thesis: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.pdf
• github(“SDS using hypercolumns”): https://github.com/bharath272/sds

Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

• arxiv: http://arxiv.org/abs/1503.00949

Weakly Supervised Object Localization Using Size Estimates

• arxiv: http://arxiv.org/abs/1608.04314

Active Object Localization with Deep Reinforcement Learning

• intro: ICCV 2015
• keywords: Markov Decision Process
• arxiv: https://arxiv.org/abs/1511.06015

Localizing objects using referring expressions

• intro: ECCV 2016
• keywords: LSTM, multiple instance learning (MIL)
• paper: http://www.umiacs.umd.edu/~varun/files/refexp-ECCV16.pdf
• github: https://github.com/varun-nagaraja/referring-expressions

LocNet: Improving Localization Accuracy for Object Detection

• intro: CVPR 2016 oral
• arxiv: http://arxiv.org/abs/1511.07763
• github: https://github.com/gidariss/LocNet

Learning Deep Features for Discriminative Localization

• homepage: http://cnnlocalization.csail.mit.edu/
• arxiv: http://arxiv.org/abs/1512.04150
• github(Tensorflow): https://github.com/jazzsaxmafia/Weakly_detector
• github: https://github.com/metalbubble/CAM
• github: https://github.com/tdeboissiere/VGG16CAM-keras

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

• intro: ECCV 2016
• project page: http://www.di.ens.fr/willow/research/contextlocnet/
• arxiv: http://arxiv.org/abs/1609.04331
• github: https://github.com/vadimkantorov/contextlocnet

Ensemble of Part Detectors for Simultaneous Classification and Localization

• arxiv: https://arxiv.org/abs/1705.10034

STNet: Selective Tuning of Convolutional Networks for Object Localization

• arxiv: https://arxiv.org/abs/1708.06418

Soft Proposal Networks for Weakly Supervised Object Localization

• intro: ICCV 2017
• arxiv: https://arxiv.org/abs/1709.01829

Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN

• intro: ACM MM 2017
• arxiv: https://arxiv.org/abs/1709.08295

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Tutorials / Talks

Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection

• slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf

Towards Good Practices for Recognition & Detection

• intro: Hikvision Research Institute. Supervised Data Augmentation (SDA)
• slides: http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf

Work in progress: Improving object detection and instance segmentation for small objects

https://docs.google.com/presentation/d/1OTfGn6mLe1VWE8D0q6Tu_WwFTSoLGd4OF8WCYnOWcVo/edit#slide=id.g37418adc7a_0_229

Object Detection with Deep Learning: A Review

• arxiv: https://arxiv.org/abs/1807.05511

---

Projects

Detectron

• intro: FAIR’s research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
• github: https://github.com/facebookresearch/Detectron

TensorBox: a simple framework for training neural networks to detect objects in images

• intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
• github: https://github.com/Russell91/TensorBox

Object detection in torch: Implementation of some object detection frameworks in torch

• github: https://github.com/fmassa/object-detection.torch

Using DIGITS to train an Object Detection network

• github: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md

FCN-MultiBox Detector

• intro: Full convolution MultiBox Detector (like SSD) implemented in Torch.
• github: https://github.com/teaonly/FMD.torch

KittiBox: A car detection model implemented in Tensorflow.

• keywords: MultiNet
• intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset
• github: https://github.com/MarvinTeichmann/KittiBox

Deformable Convolutional Networks + MST + Soft-NMS

• github: https://github.com/bharatsingh430/Deformable-ConvNets

How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow

• blog: https://towardsdatascience.com/how-to-build-a-real-time-hand-detector-using-neural-networks-ssd-on-tensorflow-d6bac0e4b2ce
• github: https://github.com//victordibia/handtracking

Metrics for object detection

• intro: Most popular metrics used to evaluate object detection algorithms
• github: https://github.com/rafaelpadilla/Object-Detection-Metrics

MobileNetv2-SSDLite

• intro: Caffe implementation of SSD and SSDLite detection on MobileNetv2, converted from tensorflow.
• github: https://github.com/chuanqi305/MobileNetv2-SSDLite

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Leaderboard

Detection Results: VOC2012

• intro: Competition “comp4” (train on additional data)
• homepage: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4

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Tools

BeaverDam: Video annotation tool for deep learning training labels

https://github.com/antingshen/BeaverDam

---

Blogs

Convolutional Neural Networks for Object Detection

http://rnd.azoft.com/convolutional-neural-networks-object-detection/

Introducing automatic object detection to visual search (Pinterest)

• keywords: Faster R-CNN
• blog: https://engineering.pinterest.com/blog/introducing-automatic-object-detection-visual-search
• demo: https://engineering.pinterest.com/sites/engineering/files/Visual%20Search%20V1%20-%20Video.mp4
• review: https://news.developer.nvidia.com/pinterest-introduces-the-future-of-visual-search/?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D

Deep Learning for Object Detection with DIGITS

• blog: https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/

Analyzing The Papers Behind Facebook’s Computer Vision Approach

• keywords: DeepMask, SharpMask, MultiPathNet
• blog: https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook’s-Computer-Vision-Approach/

Easily Create High Quality Object Detectors with Deep Learning

• intro: dlib v19.2
• blog: http://blog.dlib.net/2016/10/easily-create-high-quality-object.html

How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit

• blog: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/
• github: https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN

Object Detection in Satellite Imagery, a Low Overhead Approach

• part 1: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7#.2csh4iwx9
• part 2: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92#.f9b7dgf64

You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks

• part 1: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571#.fmmi2o3of
• part 2: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588#.nwzarsz1t

Faster R-CNN Pedestrian and Car Detection

• blog: https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
• ipn: https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0#file-demo_faster_rcnn_notebook-ipynb
• github: https://github.com/bigsnarfdude/Faster-RCNN_TF

Small U-Net for vehicle detection

• blog: https://medium.com/@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6#.md4u80kad

Region of interest pooling explained

• blog: https://deepsense.io/region-of-interest-pooling-explained/
• github: https://github.com/deepsense-io/roi-pooling

Supercharge your Computer Vision models with the TensorFlow Object Detection API

• blog: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
• github: https://github.com/tensorflow/models/tree/master/object_detection

Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning

https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab

One-shot object detection

http://machinethink.net/blog/object-detection/

An overview of object detection: one-stage methods

https://www.jeremyjordan.me/object-detection-one-stage/

deep learning object detection

• intro: A paper list of object detection using deep learning.
• github: https://github.com/hoya012/deep_learning_object_detection