Object Detection

Object Detection

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update: 2018.5.07 Mon

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1. Papers
   1. R-CNN
   2. Fast R-CNN
   3. Faster R-CNN
   4. Light-Head R-CNN
   5. Cascade R-CNN
   6. MultiBox
   7. SPP-Net
   8. MR-CNN
   9. YOLO
   10. YOLOv2
   11. YOLOv3
   12. DenseBox
   13. SSD
   14. DSSD
   15. FSSD
   16. ESSD
   17. Inside-Outside Net (ION)
   18. CRAFT
   19. OHEM
   20. R-FCN
   21. MS-CNN
   22. PVANET
   23. GBD-Net
   24. Feature Pyramid Network (FPN)
   25. DSOD
   26. RetinaNet
   27. MegDet
2. Non-Maximum Suppression (NMS)
3. Adversarial Examples
4. Weakly Supervised Object Detection
5. Video Object Detection
6. Object Detection on Mobile Devices
7. Object Detection in 3D
8. Object Detection on RGB-D
9. Zero-Shot Object Detection
10. Salient Object Detection
11. Video Saliency Detection
12. Visual Relationship Detection
13. Face Deteciton
    1. MTCNN
    2. Detect Small Faces
14. Person Head Detection
15. Pedestrian Detection / People Detection
16. Vehicle Detection
17. Traffic-Sign Detection
18. Skeleton Detection
19. Fruit Detection
    1. Shadow Detection
20. Others Detection
21. Object Proposal
22. Localization
23. Tutorials / Talks
24. Projects
25. Leaderboard
26. Tools
27. Blogs

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	VGG16		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

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

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

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

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

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

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/

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

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

YOLOv3

YOLOv3: An Incremental Improvement

• project page: https://pjreddie.com/darknet/yolo/
• arxiv: https://arxiv.org/abs/1804.02767

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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

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

ESSD

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

https://arxiv.org/abs/1801.05918

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

https://arxiv.org/abs/1802.06488

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

https://arxiv.org/abs/1705.02233

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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

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

https://arxiv.org/abs/1704.05775

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

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

Point Linking Network for Object Detection

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

Perceptual Generative Adversarial Networks for Small Object Detection

https://arxiv.org/abs/1706.05274

Few-shot Object Detection

https://arxiv.org/abs/1706.08249

Yes-Net: An effective Detector Based on Global Information

https://arxiv.org/abs/1706.09180

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

https://arxiv.org/abs/1706.10217

Towards lightweight convolutional neural networks for object detection

https://arxiv.org/abs/1707.01395

RON: Reverse Connection with Objectness Prior Networks for Object Detection

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

Mimicking Very Efficient Network for Object Detection

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

Residual Features and Unified Prediction Network for Single Stage Detection

https://arxiv.org/abs/1707.05031

Deformable Part-based Fully Convolutional Network for Object Detection

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

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

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

Recurrent Scale Approximation for Object Detection in CNN

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

DSOD

DSOD: Learning Deeply Supervised Object Detectors from Scratch

• intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
• arxiv: https://arxiv.org/abs/1708.01241
• github: https://github.com/szq0214/DSOD

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

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

https://arxiv.org/abs/1709.04347

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

https://arxiv.org/abs/1709.05788

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

https://arxiv.org/abs/1711.05187

Zero-Annotation Object Detection with Web Knowledge Transfer

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

MegDet

MegDet: A Large Mini-Batch Object Detector

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

Single-Shot Refinement Neural Network for Object Detection

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

Receptive Field Block Net for Accurate and Fast Object Detection

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

An Analysis of Scale Invariance in Object Detection - SNIP

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

Feature Selective Networks for Object Detection

https://arxiv.org/abs/1711.08879

Learning a Rotation Invariant Detector with Rotatable Bounding Box

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

Scalable Object Detection for Stylized Objects

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

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

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

Deep Regionlets for Object Detection

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

Training and Testing Object Detectors with Virtual Images

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

Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video

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

Spot the Difference by Object Detection

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

Localization-Aware Active Learning for Object Detection

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

Object Detection with Mask-based Feature Encoding

https://arxiv.org/abs/1802.03934

LSTD: A Low-Shot Transfer Detector for Object Detection

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

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

• intro: CVPR 2018. ETH Zurich & ESAT/PSI
• arxiv: https://arxiv.org/abs/1803.03243

Pseudo Mask Augmented Object Detection

https://arxiv.org/abs/1803.05858

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://arxiv.org/abs/1803.06799

Learning Region Features for Object Detection

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

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

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

Object Detection for Comics using Manga109 Annotations

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

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

https://arxiv.org/abs/1803.11316

Transferring Common-Sense Knowledge for Object Detection

https://arxiv.org/abs/1804.01077

Multi-scale Location-aware Kernel Representation for Object Detection

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

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

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

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

https://arxiv.org/abs/1804.05810

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

https://arxiv.org/abs/1711.11575

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

https://arxiv.org/abs/1711.06368

Towards High Performance Video Object Detection

https://arxiv.org/abs/1711.11577

Impression Network for Video Object Detection

https://arxiv.org/abs/1712.05896

Spatial-Temporal Memory Networks for Video Object Detection

https://arxiv.org/abs/1712.06317

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

https://arxiv.org/abs/1801.01769

Object Detection in Videos by Short and Long Range Object Linking

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

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

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

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

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

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)

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

• arxiv: https://arxiv.org/abs/1611.04849

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

https://arxiv.org/abs/1703.00152

Supervised Adversarial Networks for Image Saliency Detection

https://arxiv.org/abs/1704.07242

Group-wise Deep Co-saliency Detection

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

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

https://arxiv.org/abs/1708.06433

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

https://arxiv.org/abs/1709.02495

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

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

https://arxiv.org/abs/1708.01447

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

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

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

https://arxiv.org/abs/1711.06032

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 Networks

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

https://arxiv.org/abs/1703.10818

Face R-CNN

https://arxiv.org/abs/1706.01061

Face Detection through Scale-Friendly Deep Convolutional Networks

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

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

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

https://arxiv.org/abs/1709.05256

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

https://arxiv.org/abs/1709.07326

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

https://arxiv.org/abs/1711.07246

Feature Agglomeration Networks for Single Stage Face Detection

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

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

https://arxiv.org/abs/1804.07362

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

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

https://arxiv.org/abs/1803.09256

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

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

[https://arxiv.org/abs/1706.08564](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

https://arxiv.org/abs/1709.00235

Repulsion Loss: Detecting Pedestrians in a Crowd

https://arxiv.org/abs/1711.07752

Aggregated Channels Network for Real-Time Pedestrian Detection

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

https://arxiv.org/abs/1804.00872

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

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

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

https://arxiv.org/abs/1804.00433

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

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

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

Shadow Detection

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

https://arxiv.org/abs/1709.09283

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

https://arxiv.org/abs/1712.01361

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

https://arxiv.org/abs/1712.02478

Direction-aware Spatial Context Features for Shadow Detection

https://arxiv.org/abs/1712.04142

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

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

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

https://arxiv.org/abs/1803.04610

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

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

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

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

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

https://arxiv.org/abs/1705.10034

STNet: Selective Tuning of Convolutional Networks for Object Localization

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

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

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

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

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

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THX: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#blogs