Object Detection(目标检测论文、代码整合)

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：
20181203

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

Papers

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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

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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

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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/˜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

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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

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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

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

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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/~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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

• 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

---

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

---

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 Object Recognition.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 Detection in Videos with Tubelets and Multi-context Cues - Final.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

---

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

---

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

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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

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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

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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

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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(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: