Shallow and Deep Convolutional Networks for Saliency Prediction
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
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
Multi-view Face Detection Using Deep Convolutional Neural Networks
intro: Yahoo
arxiv:http://arxiv.org/abs/1502.02766
From Facial Parts Responses to Face Detection: A Deep Learning Approach
project page:http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html
Compact Convolutional Neural Network Cascade for Face Detection
arxiv:http://arxiv.org/abs/1508.01292
github:https://github.com/Bkmz21/FD-Evaluation
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
Supervised Transformer Network for Efficient Face Detection
arxiv:http://arxiv.org/abs/1607.05477
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
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(Matlab):https://github.com/kpzhang93/MTCNN_face_detection_alignment
github(MXNet):https://github.com/pangyupo/mxnet_mtcnn_face_detection
github:https://github.com/DaFuCoding/MTCNN_Caffe
FDDB: Face Detection Data Set and Benchmark
homepage:http://vis-www.cs.umass.edu/fddb/index.html
results:http://vis-www.cs.umass.edu/fddb/results.html
WIDER FACE: A Face Detection Benchmark
homepage:http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/
arxiv:http://arxiv.org/abs/1511.06523
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
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
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
Detecting People in Artwork with CNNs
intro: ECCV 2016 Workshops
arxiv:https://arxiv.org/abs/1610.08871
Context-aware CNNs for person head detection
arxiv:http://arxiv.org/abs/1511.07917
github:https://github.com/aosokin/cnn_head_detection
Pedestrian Detection aided by Deep Learning Semantic Tasks
intro: CVPR 2015
project page:http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/
paper: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
Deep convolutional neural networks for pedestrian detection
arxiv:http://arxiv.org/abs/1510.03608
github:https://github.com/DenisTome/DeepPed
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?
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
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
intro: ECCV 2016
arxiv:http://arxiv.org/abs/1607.04564
Traffic-Sign Detection and Classification in the Wild
project page(code+dataset):http://cg.cs.tsinghua.edu.cn/traffic-sign/
code & model:http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/newdata0411.zip
Boundary / Edge / Contour Detection
Holistically-Nested Edge Detection
intro: ICCV 2015, Marr Prize
arxiv:http://arxiv.org/abs/1504.06375
github:https://github.com/s9xie/hed
Unsupervised Learning of Edges
intro: CVPR 2016. Facebook AI Research
arxiv:http://arxiv.org/abs/1511.04166
zn-blog:http://www.leiphone.com/news/201607/b1trsg9j6GSMnjOP.html
Pushing the Boundaries of Boundary Detection using Deep Learning
arxiv:http://arxiv.org/abs/1511.07386
Convolutional Oriented Boundaries
intro: ECCV 2016
arxiv:http://arxiv.org/abs/1608.02755
Richer Convolutional Features for Edge Detection
intro: richer convolutional features (RCF)
arxiv:https://arxiv.org/abs/1612.02103
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
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
Deep Deformation Network for Object Landmark Localization
arxiv:http://arxiv.org/abs/1605.01014
Fashion Landmark Detection in the Wild
arxiv:http://arxiv.org/abs/1608.03049
Deep Learning for Fast and Accurate Fashion Item Detection
intro: Kuznech Inc.
intro: MultiBox and Fast R-CNN
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
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
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: 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
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
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
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
Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection
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 theReInspectalgorithm”
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
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
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
**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
github:https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN
Object Detection in Satellite Imagery, a Low Overhead Approach
You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks
Faster R-CNN Pedestrian and Car Detection
blog:https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/
