【转】Object Detection博客(上)

作者: Kwan_SS | 来源:发表于2017-07-09 20:57 被阅读1637次

本文转载自：https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#

数据总览

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

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 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

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
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

NoC
Object Detection Networks on Convolutional Feature Maps
intro: TPAMI 2015
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
DeepBox: Learning Objectness with Convolutional Networks
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

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

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

Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
github: https://github.com/AlexeyAB/Yolo_mark

R-CNN minus R
arxiv: http://arxiv.org/abs/1506.06981

AttentionNet
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

What’s the diffience in performance between this new code you pushed and the previous code? #327
https://github.com/weiliu89/caffe/issues/327
Enhancement of SSD by concatenating feature maps for object detection
intro: rainbow SSD (R-SSD)
arxiv: https://arxiv.org/abs/1705.09587

DSSD
DSSD : Deconvolutional Single Shot Detector
intro: UNC Chapel Hill & Amazon Inc
arxiv: https://arxiv.org/abs/1701.06659

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
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
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
arxiv: http://arxiv.org/abs/1604.00600

MultiPathNet
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/

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: 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

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: Deep but Lightweight Neural Networks for Real-time Object Detection
intro: “less channels with more layers”, concatenated ReLU, Inception, and HyperNet, batch normalization, residual connections
arxiv: http://arxiv.org/abs/1608.08021
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

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

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
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: 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

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

CC-Net
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
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
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
S-OHEM: Stratified Online Hard Example Mining for Object Detection
https://arxiv.org/abs/1705.02233
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
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

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

Learning non-maximum suppression
https://arxiv.org/abs/1705.02950
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

Detection From Video
****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
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

Object Detection in 3D
****Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks**
arxiv: https://arxiv.org/abs/1609.06666

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
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
Saliency Detection in Video
Deep Learning For Video Saliency Detection
arxiv: https://arxiv.org/abs/1702.00871

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
Specific Object Deteciton
**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

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: https://github.com/peiyunh/tiny
github(inference-only): https://github.com/chinakook/hr101_mxnet

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
UnitBox: An Advanced Object Detection Network
intro: ACM MM 2016
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

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

Facial Point / Landmark Detection
Deep Convolutional Network Cascade for Facial Point Detection

homepage: http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm
paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr13.pdf
github: https://github.com/luoyetx/deep-landmark

Facial Landmark Detection by Deep Multi-task Learning
intro: ECCV 2014
project page: http://mmlab.ie.cuhk.edu.hk/projects/TCDCN.html
paper: http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepfacealign.pdf
github(Matlab): https://github.com/zhzhanp/TCDCN-face-alignment

A Recurrent Encoder-Decoder Network for Sequential Face Alignment
intro: ECCV 2016
arxiv: https://arxiv.org/abs/1608.05477

Detecting facial landmarks in the video based on a hybrid framework
arxiv: http://arxiv.org/abs/1609.06441

Deep Constrained Local Models for Facial Landmark Detection
arxiv: https://arxiv.org/abs/1611.08657

Effective face landmark localization via single deep network
arxiv: https://arxiv.org/abs/1702.02719

A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection
https://arxiv.org/abs/1704.01880
Deep Alignment Network: A convolutional neural network for robust face alignment
intro: CVPRW 2017
arxiv: https://arxiv.org/abs/1706.01789
gihtub: https://github.com/MarekKowalski/DeepAlignmentNetwork

People Detection
****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

Detecting People in Artwork with CNNs
intro: ECCV 2016 Workshops
arxiv: https://arxiv.org/abs/1610.08871

Deep Multi-camera People Detection
arxiv: https://arxiv.org/abs/1702.04593

Person Head Detection
Context-aware CNNs for person head detection
arxiv: http://arxiv.org/abs/1511.07917
github: https://github.com/aosokin/cnn_head_detection

Pedestrian 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

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

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

Multispectral Deep Neural Networks for Pedestrian Detection
intro: BMVC 2016 oral
arxiv: https://arxiv.org/abs/1611.02644

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

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

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

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