图像滤镜艺术---换脸算法资源收集
https://github.com/Guikunzhi/BeautifyFaceDemo
https://github.com/alyssaq/face_morpher
http://life-in-a-monospace-typeface.tumblr.com/post/28495219189/quick-and-dirty-automatic-face-replacement-with
Image Morphing - local (non-parametric) warp
http://alumni.media.mit.edu/~maov/classes/comp_photo_vision08f/lect/07_Image%20Morphing.pdf
https://github.com/arturoc/FaceSubstitution
https://github.com/jorticus/face-replace
https://github.com/t0nyren/piecewiseAffine
https://github.com/royshil/HeadReplacement
https://github.com/trishume/faceHack
https://github.com/marsbroshok/face-replace
https://github.com/junyanz/FaceDemo
https://github.com/matthewearl/faceswap
http://home.elka.pw.edu.pl/%7Emkowals6/doku.php
https://github.com/takiyu/CLM
https://github.com/mc-jesus/FaceSwap
https://matthewearl.github.io/2015/07/28/switching-eds-with-python/
https://github.com/MarekKowalski/FaceSwap
http://alumni.media.mit.edu/~roys/identitytransfer-cgaieee2012/
https://github.com/HVisionSensing/FaceFlip
https://github.com/liaojing/Image-Morphing/tree/master/code
https://github.com/blendmaster/rigid-faces
https://github.com/hrastnik/FaceSwap
https://github.com/spmallick/learnopencv/tree/master/FaceMorph
https://github.com/spmallick/learnopencv/tree/master/FaceSwap
https://github.com/YuvalNirkin/face_swap
https://github.com/menpo/lsfm
#Deep learning
https://github.com/ddtm/deep-smile-warp
https://github.com/msracver/Deep-Image-Analogy
https://github.com/datitran/face2face-demo
https://github.com/ZZUTK/Face-Aging-CAAE
https://github.com/zo7/deconvfaces3D https://github.com/KeeganRen/FaceReconstruction https://github.com/anhttran/3dmm_cnn Video: https://github.com/YuvalNirkin/face_video_segmentDeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations
- arxiv: http://arxiv.org/abs/1510.02927
Some like it hot - visual guidance for preference prediction
- arxiv: http://arxiv.org/abs/1510.07867
- demo: http://howhot.io/
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
- arxiv: http://arxiv.org/abs/1512.03131
Deep Relative Attributes
- intro: ACCV 2016
- arxiv: http://arxiv.org/abs/1512.04103
- github: https://github.com/yassersouri/ghiaseddin
Deep-Spying: Spying using Smartwatch and Deep Learning
- arxiv: http://arxiv.org/abs/1512.05616
- github: https://github.com/tonybeltramelli/Deep-Spying
Camera identification with deep convolutional networks
- key word: copyright infringement cases, ownership attribution
- arxiv: http://arxiv.org/abs/1603.01068
An Analysis of Deep Neural Network Models for Practical Applications
- arxiv: http://arxiv.org/abs/1605.07678
8 Inspirational Applications of Deep Learning
- intro: Colorization of Black and White Images, Adding Sounds To Silent Movies, Automatic Machine Translation Object Classification in Photographs, Automatic Handwriting Generation, Character Text Generation, Image Caption Generation, Automatic Game Playing
- blog: http://machinelearningmastery.com/inspirational-applications-deep-learning/
16 Open Source Deep Learning Models Running as Microservices
- intro: Places 365 Classifier, Deep Face Recognition, Real Estate Classifier, Colorful Image Colorization, Illustration Tagger, InceptionNet, Parsey McParseface, ArtsyNetworks
- blog: http://blog.algorithmia.com/2016/07/open-source-deep-learning-algorithm-roundup/
Deep Cascaded Bi-Network for Face Hallucination
- project page: http://mmlab.ie.cuhk.edu.hk/projects/CBN.html
- arxiv: http://arxiv.org/abs/1607.05046
DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation
- project page: http://yaroslav.ganin.net/static/deepwarp/
- arxiv: http://arxiv.org/abs/1607.07215
Autoencoding Blade Runner
- blog: https://medium.com/@Terrybroad/autoencoding-blade-runner-88941213abbe#.9kckqg7cq
- github: https://github.com/terrybroad/Learned-Sim-Autoencoder-For-Video-Frames
A guy trained a machine to "watch" Blade Runner. Then things got seriously sci-fi.
http://www.vox.com/2016/6/1/11787262/blade-runner-neural-network-encoding
Deep Convolution Networks for Compression Artifacts Reduction
- intro: ICCV 2015
- project page(code): http://mmlab.ie.cuhk.edu.hk/projects/ARCNN.html
- arxiv: http://arxiv.org/abs/1608.02778
Deep GDashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
- intro: Deep Genomic Dashboard (Deep GDashboard)
- arxiv: http://arxiv.org/abs/1608.03644
Instagram photos reveal predictive markers of depression
- arxiv: http://arxiv.org/abs/1608.03282
How an Algorithm Learned to Identify Depressed Individuals by Studying Their Instagram Photos
- review: https://www.technologyreview.com/s/602208/how-an-algorithm-learned-to-identify-depressed-individuals-by-studying-their-instagram/
IM2CAD
- arxiv: http://arxiv.org/abs/1608.05137
Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks
- paper: https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/melicher
- github: https://github.com/cupslab/neural_network_cracking
Defeating Image Obfuscation with Deep Learning
- arxiv: http://arxiv.org/abs/1609.00408
Detecting Music BPM using Neural Networks
- keywords: BPM (Beats Per Minutes)
- blog: https://nlml.github.io/neural-networks/detecting-bpm-neural-networks/
- github: https://github.com/nlml/bpm
Generative Visual Manipulation on the Natural Image Manifold
- intro: ECCV 2016
- project page: https://people.eecs.berkeley.edu/~junyanz/projects/gvm/
- arxiv: http://arxiv.org/abs/1609.03552
- github: https://github.com/junyanz/iGAN
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition
- arxiv: http://arxiv.org/abs/1609.05119
Deep Gold: Using Convolution Networks to Find Minerals
- blog: https://hackernoon.com/deep-gold-using-convolution-networks-to-find-minerals-aafdb37355df#.lgh95ub4a
- github: https://github.com/scottvallance/DeepGold
Predicting First Impressions with Deep Learning
- arxiv: https://arxiv.org/abs/1610.08119
Judging a Book By its Cover
- arxiv: https://arxiv.org/abs/1610.09204
- review: https://www.technologyreview.com/s/602807/deep-neural-network-learns-to-judge-books-by-their-covers/
Image Credibility Analysis with Effective Domain Transferred Deep Networks
- arxiv: https://arxiv.org/abs/1611.05328
A novel image tag completion method based on convolutional neural network
- arxiv: https://www.arxiv.org/abs/1703.00586
Image operator learning coupled with CNN classification and its application to staff line removal
- intro: ICDAR 2017
- arxiv: https://arxiv.org/abs/1709.06476
Joint Image Filtering with Deep Convolutional Networks
- intro: University of California, Merced & Virginia Tech & University of Illinois
- project page: http://vllab1.ucmerced.edu/~yli62/DJF_residual/
- arxiv: https://arxiv.org/abs/1710.04200
- github: https://github.com/Yijunmaverick/DeepJointFilter
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1704.02470
- github: https://github.com/aiff22/DPED
Image-Text
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
https://arxiv.org/abs/1704.03470
Dual-Path Convolutional Image-Text Embedding
https://arxiv.org/abs/1711.05535
Age Estimation
Deeply-Learned Feature for Age Estimation
- paper: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7045931&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7045931
Age and Gender Classification using Convolutional Neural Networks
- paper: http://www.openu.ac.il/home/hassner/projects/cnn_agegender/CNN_AgeGenderEstimation.pdf
- project page: http://www.openu.ac.il/home/hassner/projects/cnn_agegender/
- github: https://github.com/GilLevi/AgeGenderDeepLearning
Group-Aware Deep Feature Learning For Facial Age Estimation
- paper: http://www.sciencedirect.com/science/article/pii/S0031320316303417
Local Deep Neural Networks for Age and Gender Classification
https://arxiv.org/abs/1703.08497
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
https://arxiv.org/abs/1708.07689
Age Group and Gender Estimation in the Wild with Deep RoR Architecture
- intro: IEEE ACCESS
- arxiv: https://arxiv.org/abs/1710.02985
Face Aging
Recurrent Face Aging
- paper: www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf
Face Aging With Conditional Generative Adversarial Networks
- arxiv: https://arxiv.org/abs/1702.01983
Emotion Recognition / Expression Recognition
Real-time emotion recognition for gaming using deep convolutional network features
- paper: http://arxiv.org/abs/1408.3750v1
- code: https://github.com/Zebreu/ConvolutionalEmotion
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
- project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/
- paper: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/LeviHassnerICMI15.pdf
- github: https://gist.github.com/GilLevi/54aee1b8b0397721aa4b
- blog: https://gilscvblog.com/2017/01/31/emotion-recognition-in-the-wild-via-convolutional-neural-networks-and-mapped-binary-patterns/
DeXpression: Deep Convolutional Neural Network for Expression Recognition
- paper: http://arxiv.org/abs/1509.05371
DEX: Deep EXpectation of apparent age from a single image
- intro: ICCV 2015
- paper: https://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_01229.pdf
- homepage: https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/
EmotioNet: EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild
- intro: CVPR 2016
- paper: http://cbcsl.ece.ohio-state.edu/cvpr16.pdf
- database: http://cbcsl.ece.ohio-state.edu/dbform_emotionet.html
How Deep Neural Networks Can Improve Emotion Recognition on Video Data
- intro: ICIP 2016
- arxiv: http://arxiv.org/abs/1602.07377
Peak-Piloted Deep Network for Facial Expression Recognition
- arxiv: http://arxiv.org/abs/1607.06997
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
- arxiv: http://arxiv.org/abs/1608.01041
A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset
- arxiv: http://arxiv.org/abs/1608.01647
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
- arxiv: http://arxiv.org/abs/1609.06591
EmotionNet Challenge
- homrepage: http://cbcsl.ece.ohio-state.edu/EmotionNetChallenge/index.html
- dataset: http://cbcsl.ece.ohio-state.edu/dbform_emotionet.html
Baseline CNN structure analysis for facial expression recognition
- intro: RO-MAN2016 Conference
- arxiv: https://arxiv.org/abs/1611.04251
Facial Expression Recognition using Convolutional Neural Networks: State of the Art
- arxiv: https://arxiv.org/abs/1612.02903
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
- arxiv: https://arxiv.org/abs/1702.04280
- api: https://www.sighthound.com/products/cloud
Deep generative-contrastive networks for facial expression recognition
https://arxiv.org/abs/1703.07140
Convolutional Neural Networks for Facial Expression Recognition
https://arxiv.org/abs/1704.06756
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
- intro: Imperial College London
- arxiv: https://arxiv.org/abs/1704.08619
Spatial-Temporal Recurrent Neural Network for Emotion Recognition
https://arxiv.org/abs/1705.04515
Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units
https://arxiv.org/abs/1706.01509
Temporal Multimodal Fusion for Video Emotion Classification in the Wild
https://arxiv.org/abs/1709.07200
Island Loss for Learning Discriminative Features in Facial Expression Recognition
https://arxiv.org/abs/1710.03144
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://arxiv.org/abs/1710.07557
Attribution Prediction
PANDA: Pose Aligned Networks for Deep Attribute Modeling
- intro: Facebook. CVPR 2014
- arxiv: http://arxiv.org/abs/1311.5591
- github: https://github.com/facebook/pose-aligned-deep-networks
Predicting psychological attributions from face photographs with a deep neural network
- arxiv: http://arxiv.org/abs/1512.01289
Learning Human Identity from Motion Patterns
- arxiv: http://arxiv.org/abs/1511.03908
Pose Estimation
DeepPose: Human Pose Estimation via Deep Neural Networks
- intro: CVPR 2014
- arxiv: http://arxiv.org/abs/1312.4659
- slides: http://140.122.184.143/paperlinks/Slides/DeepPose_HumanPose_Estimation_via_Deep_Neural_Networks.pptx
- github: https://github.com/asanakoy/deeppose_tf
Heterogeneous multi-task learning for human pose estimation with deep convolutional neural network
- paper: www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W15/papers/LI_Heterogeneous_Multi-task_Learning_2014_CVPR_paper.pdf
Flowing ConvNets for Human Pose Estimation in Videos
- arxiv: http://arxiv.org/abs/1506.02897
- homepage: http://www.robots.ox.ac.uk/~vgg/software/cnn_heatmap/
- github: https://github.com/tpfister/caffe-heatmap
Structured Feature Learning for Pose Estimation
- arxiv: http://arxiv.org/abs/1603.09065
- homepage: http://www.ee.cuhk.edu.hk/~xgwang/projectpage_structured_feature_pose.html
Convolutional Pose Machines
- arxiv: http://arxiv.org/abs/1602.00134
- github: https://github.com/shihenw/convolutional-pose-machines-release
- github(PyTorch): https://github.com/tensorboy/pytorch_Realtime_Multi-Person_Pose_Estimation
Model-based Deep Hand Pose Estimation
- paper: http://xingyizhou.xyz/zhou2016model.pdf
- github: https://github.com/tenstep/DeepModel
Stacked Hourglass Networks for Human Pose Estimation
- homepage: http://www-personal.umich.edu/~alnewell/pose/
- arxiv: http://arxiv.org/abs/1603.06937
- github: https://github.com/anewell/pose-hg-train
- demo: https://github.com/anewell/pose-hg-demo
Chained Predictions Using Convolutional Neural Networks
- intro: EECV 2016
- keywords: CNN, structured prediction, RNN, human pose estimation
- arxiv: http://arxiv.org/abs/1605.02346
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
- arxiv: http://arxiv.org/abs/1605.03170
- github: https://github.com/eldar/deepcut-cnn
Real-time Human Pose Estimation from Video with Convolutional Neural Networks
- arxiv: http://arxiv.org/abs/1609.07420
Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation
- arxiv: https://arxiv.org/abs/1702.02447
Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources
- project page: https://www.adrianbulat.com/binary-cnn-landmarks
- arxiv: https://www.arxiv.org/abs/1703.00862
Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
- arxiv: https://arxiv.org/abs/1705.00389
- video: http://v.qq.com/x/page/c039862eira.html
- video: http://v.qq.com/x/page/f0398zcvkl5.html
- video: http://v.qq.com/x/page/w0398ei9m1r.html
Human Pose Detection Mining Body Language from Videos
- blog: https://medium.com/@samim/human-pose-detection-51268e95ddc2
OpenPose: A Real-Time Multi-Person Keypoint Detection And Multi-Threading C++ Library
- intro: OpenPose is a library for real-time multi-person keypoint detection and multi-threading written in C++ using OpenCV and Caffe
- github: https://github.com/CMU-Perceptual-Computing-Lab/openpose
Learning Feature Pyramids for Human Pose Estimation
- arxiv: https://arxiv.org/abs/1708.01101
- github: https://github.com/bearpaw/PyraNet
Multi-Context Attention for Human Pose Estimation
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1702.07432
- github(Torch): https://github.com/bearpaw/pose-attention
Human Pose Estimation with TensorFlow
https://github.com/eldar/pose-tensorflow
Cascaded Pyramid Network for Multi-Person Pose Estimation
- intro: Tsinghua University & HuaZhong Univerisity of Science and Technology & Megvii Inc
- github: https://arxiv.org/abs/1711.07319
Sentiment Analysis / Sentiment Prediction
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
- arxiv: http://arxiv.org/abs/1604.03489
- github: https://github.com/imatge-upc/sentiment-2016
- gitxiv: http://gitxiv.com/posts/ruqRgXdPTHJ77LDEb/from-pixels-to-sentiment-fine-tuning-cnns-for-visual
Predict Sentiment From Movie Reviews Using Deep Learning
- blog: http://machinelearningmastery.com/predict-sentiment-movie-reviews-using-deep-learning/
Neural Sentiment Classification with User and Product Attention
- intro: EMNLP 2016
- paper: http://www.thunlp.org/~chm/publications/emnlp2016_NSCUPA.pdf
- github: https://github.com/thunlp/NSC
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
- intro: Accepted for publication in Image and Vision Computing
- arxiv: https://arxiv.org/abs/1604.03489
- github: https://github.com/imatge-upc/sentiment-2016
Visual and Textual Sentiment Analysis Using Deep Fusion Convolutional Neural Networks
- intro: ICIP 2017
- arxiv: https://arxiv.org/abs/1711.07798
Place Recognition
NetVLAD: CNN architecture for weakly supervised place recognition
- intro: CVPR 2016
- intro: Google Street View Time Machine, soft-assignment, Weakly supervised triplet ranking loss
- homepage: http://www.di.ens.fr/willow/research/netvlad/
- arxiv: http://arxiv.org/abs/1511.07247
PlaNet - Photo Geolocation with Convolutional Neural Networks
- arxiv: http://arxiv.org/abs/1602.05314
- review("Google Unveils Neural Network with “Superhuman” Ability to Determine the Location of Almost Any Image"): https://www.technologyreview.com/s/600889/google-unveils-neural-network-with-superhuman-ability-to-determine-the-location-of-almost/
- github("City-Recognition: CS231n Project for Winter 2016"): https://github.com/dmakian/LittlePlaNet
- github: https://github.com/wulfebw/LittlePlaNet-Models
Visual place recognition using landmark distribution descriptors
- arxiv: http://arxiv.org/abs/1608.04274
Low-effort place recognition with WiFi fingerprints using deep learning
- arxiv: https://arxiv.org/abs/1611.02049
- github: https://github.com/aqibsaeed/Place-Recognition-using-Autoencoders-and-NN
- github(Keras): https://github.com/mallsk23/place_recognition_wifi_fingerprints_deep_learning
Deep Learning Features at Scale for Visual Place Recognition
- intro: ICRA 2017
- arxiv: https://arxiv.org/abs/1701.05105
Place recognition: An Overview of Vision Perspective
https://arxiv.org/abs/1707.03470
Camera Relocalization
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
- paper: http://arxiv.org/abs/1505.07427
- project page: http://mi.eng.cam.ac.uk/projects/relocalisation/#results
- github: https://github.com/alexgkendall/caffe-posenet
- github(TensorFlow): https://github.com/kentsommer/tensorflow-posenet
Modelling Uncertainty in Deep Learning for Camera Relocalization
- paper: http://arxiv.org/abs/1509.05909
Random Forests versus Neural Networks - What's Best for Camera Relocalization?
- arxiv: http://arxiv.org/abs/1609.05797
Deep Convolutional Neural Network for 6-DOF Image Localization
- arxiv: https://arxiv.org/abs/1611.02776
Image-based Localization with Spatial LSTMs
- arxiv: https://arxiv.org/abs/1611.07890
VidLoc: 6-DoF Video-Clip Relocalization
- arxiv: https://arxiv.org/abs/1702.06521
Towards CNN Map Compression for camera relocalisation
- arxiv: https://www.arxiv.org/abs/1703.00845
Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network
- intro: Aalto University & Indian Institute of Technology
- arxiv: https://arxiv.org/abs/1707.09733
Counting Objects
Towards perspective-free object counting with deep learning
- intro: ECCV 2016. Counting CNN and Hydra CNN
- paper: http://agamenon.tsc.uah.es/Investigacion/gram/publications/eccv2016-onoro.pdf
- github: https://github.com/gramuah/ccnn
- poster: http://www.eccv2016.org/files/posters/P-3B-26.pdf
Using Convolutional Neural Networks to Count Palm Trees in Satellite Images
- arxiv: https://arxiv.org/abs/1701.06462
Count-ception: Counting by Fully Convolutional Redundant Counting
https://arxiv.org/abs/1703.08710
Counting Objects with Faster R-CNN
- blog: https://softwaremill.com/counting-objects-with-faster-rcnn/
- github: https://github.com/softberries/keras-frcnn
Drone-based Object Counting by Spatially Regularized Regional Proposal Network
https://arxiv.org/abs/1707.05972
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
- intro: ICCV 2017. CMU & Universidade de Lisboa
- arxiv: https://arxiv.org/abs/1707.09476
Representation Learning by Learning to Count
- intro: ICCV 2017 oral
- arxiv: https://arxiv.org/abs/1708.06734
Leaf Counting with Deep Convolutional and Deconvolutional Networks
- intro: ICCV 2017 Workshop on Computer Vision Problems in Plant Phenotyping
- arxiv: https://arxiv.org/abs/1708.07570
Crowd Counting / Crowd Analysis
Large scale crowd analysis based on convolutional neural network
- paper: http://www.sciencedirect.com/science/article/pii/S0031320315001259
Deep People Counting in Extremely Dense Crowds
- intro: ACM 2015
- paper: http://yangliang.github.io/pdf/sp055u.pdf
Crossing-line Crowd Counting with Two-phase Deep Neural Networks
- intro: ECCV 2016
- paper: http://www.ee.cuhk.edu.hk/~rzhao/project/crossline_eccv16/ZhaoLZWeccv16.pdf
- poster: http://www.eccv2016.org/files/posters/P-3C-41.pdf
Cross-scene Crowd Counting via Deep Convolutional Neural Networks
- intro: CVPR 2015
- paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/zhangLWYcvpr15.pdf
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
- intro: CVPR 2016
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Single-Image_Crowd_Counting_CVPR_2016_paper.pdf
- paper: http://sist.shanghaitech.edu.cn/office/research/news/CVPR2016/paper/Single-Image%20Crowd%20Counting%20via%20Multi-Column%20Convolutional%20Neural%20Network.pdf
- dataset(pwd: p1rv): http://pan.baidu.com/s/1gfyNBTh
- slides: http://smartdsp.xmu.edu.cn/%E6%B1%87%E6%8A%A5pdf/crowd%20counting%E6%9E%97%E8%B4%A8%E9%94%90.pdf
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
- intro: ACM Multimedia (MM) 2016
- arxiv: http://arxiv.org/abs/1608.06197
Crowd Counting by Adapting Convolutional Neural Networks with Side Information
- arxiv: https://arxiv.org/abs/1611.06748
Fully Convolutional Crowd Counting On Highly Congested Scenes
- intro: VISAPP 2017
- arxiv: https://arxiv.org/abs/1612.00220
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
- intro: AAAI 2017
- project page: https://www.microsoft.com/en-us/research/publication/deep-spatio-temporal-residual-networks-for-citywide-crowd-flows-prediction/
- paper: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/ST-ResNet-AAAI17-Zhang.pdf
- github: https://github.com/lucktroy/DeepST/tree/master/scripts/papers/AAAI17
- ppt: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/DeepST-crowd-prediction.pptx
- system: http://urbanflow.sigkdd.com.cn/
Multi-scale Convolutional Neural Networks for Crowd Counting
- arxiv: https://arxiv.org/abs/1702.02359
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting
https://arxiv.org/abs/1703.09393
Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking
https://arxiv.org/abs/1705.10118
ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification
- intro: AVSS 2017
- arxiv: https://arxiv.org/abs/1705.10698
Image Crowd Counting Using Convolutional Neural Network and Markov Random Field
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1706.03725
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation
https://arxiv.org/abs/1707.01202
Spatiotemporal Modeling for Crowd Counting in Videos
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1707.07890
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
- intro: AVSS 2017 (14th International Conference on Advanced Video and Signal Based Surveillance)
- arxiv: https://arxiv.org/abs/1707.09605
Switching Convolutional Neural Network for Crowd Counting
- intro: CVPR 2017. Indian Institute of Science
- project page: http://val.serc.iisc.ernet.in/crowdcnn/
- arxiv: https://arxiv.org/abs/1708.00199
- github: https://github.com/val-iisc/crowd-counting-scnn
Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1708.00953
Deep Spatial Regression Model for Image Crowd Counting
https://arxiv.org/abs/1710.09757
Crowd counting via scale-adaptive convolutional neural network
- intro: Tencent Youtu Lab
- arxiv: https://arxiv.org/abs/1711.04433
Activity Recognition
Implementing a CNN for Human Activity Recognition in Tensorflow
- blog: http://aqibsaeed.github.io/2016-11-04-human-activity-recognition-cnn/
- github: https://github.com/aqibsaeed/Human-Activity-Recognition-using-CNN
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure
- arxiv: https://arxiv.org/abs/1702.01638
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1704.03058
Deploying Tensorflow model on Andorid device for Human Activity Recognition
- blog: http://aqibsaeed.github.io/2017-05-02-deploying-tensorflow-model-andorid-device-human-activity-recognition/
- github: https://github.com/aqibsaeed/Human-Activity-Recognition-using-CNN/tree/master/ActivityRecognition
Music Classification / Sound Classification
Explaining Deep Convolutional Neural Networks on Music Classification
- arxiv: http://arxiv.org/abs/1607.02444
- blog: https://keunwoochoi.wordpress.com/2015/12/09/ismir-2015-lbd-auralisation-of-deep-convolutional-neural-networks-listening-to-learned-features-auralization/
- blog: https://keunwoochoi.wordpress.com/2016/03/23/what-cnns-see-when-cnns-see-spectrograms/
- github: https://github.com/keunwoochoi/Auralisation
- audio samples: https://soundcloud.com/kchoi-research
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
- project page: http://www.stat.ucla.edu/~yang.lu/project/deepFrame/main.html
- arxiv: http://arxiv.org/abs/1608.04363
Convolutional Recurrent Neural Networks for Music Classification
- arxiv: http://arxiv.org/abs/1609.04243
- blog: https://keunwoochoi.wordpress.com/2016/09/15/paper-is-out-convolutional-recurrent-neural-networks-for-music-classification/
- github: https://github.com/keunwoochoi/music-auto_tagging-keras
CNN Architectures for Large-Scale Audio Classification
- intro: Google
- arxiv: https://arxiv.org/abs/1609.09430
- demo: https://www.youtube.com/watch?v=oAAo_r7ZT8U&feature=youtu.be
SoundNet: Learning Sound Representations from Unlabeled Video
- intro: MIT. NIPS 2016
- project page: http://projects.csail.mit.edu/soundnet/
- arxiv: https://arxiv.org/abs/1610.09001
- paper: http://web.mit.edu/vondrick/soundnet.pdf
- github: https://github.com/cvondrick/soundnet
- github: https://github.com/eborboihuc/SoundNet-tensorflow
- youtube: https://www.youtube.com/watch?v=yJCjVvIY4dU
Deep Learning 'ahem' detector
- github: https://github.com/worldofpiggy/deeplearning-ahem-detector
- slides: https://docs.google.com/presentation/d/1QXQEOiAMj0uF2_Gafr2bn-kMniUJAIM1PLTFm1mUops/edit#slide=id.g35f391192_00
- mirror: https://pan.baidu.com/s/1c2KGlwO
GenreFromAudio: Finding the genre of a song with Deep Learning
- intro: A pipeline to build a dataset from your own music library and use it to fill the missing genres
- github: https://github.com/despoisj/DeepAudioClassification
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
- arxiv: https://arxiv.org/abs/1703.10667
- github: https://github.com/chihyaoma/Activity-Recognition-with-CNN-and-RNN
On the Robustness of Deep Convolutional Neural Networks for Music Classification
- intro: Queen Mary University of London & New York University
- arxiv: https://arxiv.org/abs/1706.02361
NSFW Detection / Classification
Nipple Detection using Convolutional Neural Network
- reddit: https://www.reddit.com/over18?dest=https%3A%2F%2Fwww.reddit.com%2Fr%2FMachineLearning%2Fcomments%2F33n77s%2Fandroid_app_nipple_detection_using_convolutional%2F
Applying deep learning to classify pornographic images and videos
- arxiv: http://arxiv.org/abs/1511.08899
MODERATE, FILTER, OR CURATE ADULT CONTENT WITH CLARIFAI’S NSFW MODEL
- blog: http://blog.clarifai.com/moderate-filter-or-curate-adult-content-with-clarifais-nsfw-model/#.VzVhM-yECZY
WHAT CONVOLUTIONAL NEURAL NETWORKS LOOK AT WHEN THEY SEE NUDITY
- blog: http://blog.clarifai.com/what-convolutional-neural-networks-see-at-when-they-see-nudity#.VzVh_-yECZY
Open Sourcing a Deep Learning Solution for Detecting NSFW Images
- intro: Yahoo
- blog: https://yahooeng.tumblr.com/post/151148689421/open-sourcing-a-deep-learning-solution-for
- github: https://github.com/yahoo/open_nsfw
Miles Deep - AI Porn Video Editor
- intro: Deep Learning Porn Video Classifier/Editor with Caffe
- github: https://github.com/ryanjay0/miles-deep
Image Reconstruction / Inpainting
Context Encoders: Feature Learning by Inpainting
- intro: CVPR 2016
- intro: Unsupervised Feature Learning by Image Inpainting using GANs
- project page: http://www.cs.berkeley.edu/~pathak/context_encoder/
- arxiv: https://arxiv.org/abs/1604.07379
- github(official): https://github.com/pathak22/context-encoder
- github: https://github.com/BoyuanJiang/context_encoder_pytorch
Semantic Image Inpainting with Perceptual and Contextual Losses
Semantic Image Inpainting with Deep Generative Models
- keywords: Deep Convolutional Generative Adversarial Network (DCGAN)
- arxiv: http://arxiv.org/abs/1607.07539
- github: https://github.com/bamos/dcgan-completion.tensorflow
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
- intro: University of Southern California & Adobe Research
- arxiv: https://arxiv.org/abs/1611.09969
Face Image Reconstruction from Deep Templates
https://www.arxiv.org/abs/1703.00832
Deep Learning-Guided Image Reconstruction from Incomplete Data
https://arxiv.org/abs/1709.00584
Learning to Inpaint for Image Compression
https://arxiv.org/abs/1709.08855
Image Restoration
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
- intro: NIPS 2016
- arxiv: http://arxiv.org/abs/1603.09056
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
- arxiv: http://arxiv.org/abs/1606.08921
Image Completion with Deep Learning in TensorFlow
- blog: http://bamos.github.io/2016/08/09/deep-completion/
Deeply Aggregated Alternating Minimization for Image Restoration
- arxiv: https://arxiv.org/abs/1612.06508
A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction
- intro: Seoul National University
- arxiv: https://arxiv.org/abs/1701.06190
Generative Face Completion
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1704.05838
MemNet: A Persistent Memory Network for Image Restoration
- intro: ICCV 2017 (Spotlight presentation)
- arxiv: https://arxiv.org/abs/1708.02209
- github: https://github.com/tyshiwo/MemNet
Deep Mean-Shift Priors for Image Restoration
- intro: NIPS 2017
- arxiv: https://arxiv.org/abs/1709.03749
xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
https://arxiv.org/abs/1711.06445
Image Super-Resolution
Super-Resolution.Benckmark
- intro: Benchmark and resources for single super-resolution algorithms
- github: https://github.com/huangzehao/Super-Resolution.Benckmark
Image Super-Resolution Using Deep Convolutional Networks
- intro: Microsoft Research
- project page: http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html
- arxiv: http://arxiv.org/abs/1501.00092
- training code: http://mmlab.ie.cuhk.edu.hk/projects/SRCNN/SRCNN_train.zip
- test code: http://mmlab.ie.cuhk.edu.hk/projects/SRCNN/SRCNN_v1.zip
- github(Keras): https://github.com/titu1994/Image-Super-Resolution
Learning a Deep Convolutional Network for Image Super-Resolution
- Baidu-pan: http://pan.baidu.com/s/1c0k0wRu
Shepard Convolutional Neural Networks
- paper: https://papers.nips.cc/paper/5774-shepard-convolutional-neural-networks.pdf
- github: https://github.com/jimmy-ren/vcnn_double-bladed/tree/master/applications/Shepard_CNN
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution
- intro: NIPS 2015
- paper: https://papers.nips.cc/paper/5778-bidirectional-recurrent-convolutional-networks-for-multi-frame-super-resolution
Deeply-Recursive Convolutional Network for Image Super-Resolution
- intro: CVPR 2016
- arxiv: http://arxiv.org/abs/1511.04491
- paper: http://cv.snu.ac.kr/publication/conf/2016/DRCN_CVPR2016.pdf
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- intro: CVPR 2016 Oral
- project page: http://cv.snu.ac.kr/research/VDSR/
- arxiv: http://arxiv.org/abs/1511.04587
- code: http://cv.snu.ac.kr/research/VDSR/VDSR_code.zip
- github: https://github.com/huangzehao/caffe-vdsr
- github(Torch): https://github.com/pby5/vdsr_torch
Super-Resolution with Deep Convolutional Sufficient Statistics
- arxiv: http://arxiv.org/abs/1511.05666
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
- arxiv: http://arxiv.org/abs/1607.01977
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
- arxiv: http://arxiv.org/abs/1607.07220
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
- arxiv: http://arxiv.org/abs/1607.07680
Accelerating the Super-Resolution Convolutional Neural Network
- intro: speed up of more than 40 times with even superior restoration quality, real-time performance on a generic CPU
- project page: http://mmlab.ie.cuhk.edu.hk/projects/FSRCNN.html
- arxiv: http://arxiv.org/abs/1608.00367
srez: Image super-resolution through deep learning
- github: https://github.com/david-gpu/srez
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
- arxiv: https://arxiv.org/abs/1609.04802
- github(Torch): https://github.com/leehomyc/Photo-Realistic-Super-Resoluton
- github: https://github.com/junhocho/SRGAN
- github(Keras): https://github.com/titu1994/Super-Resolution-using-Generative-Adversarial-Networks
- github: https://github.com/buriburisuri/SRGAN
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
- intro: CVPR 2016
- arxiv: http://arxiv.org/abs/1609.05158
- github: https://github.com/Tetrachrome/subpixel
Is the deconvolution layer the same as a convolutional layer?
- intro: A note on RealTime Single Image and Video SuperResolution Using an Efficient SubPixel Convolutional Neural Network.
- arxiv: http://arxiv.org/abs/1609.07009
Amortised MAP Inference for Image Super-resolution
- arxiv: https://arxiv.org/abs/1610.04490
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
- arxiv: https://arxiv.org/abs/1611.05250
Super-Resolution on Satellite Imagery using Deep Learning
- part 1: https://medium.com/the-downlinq/super-resolution-on-satellite-imagery-using-deep-learning-part-1-ec5c5cd3cd2#.4oxn9pafu
Neural Enhance: Super Resolution for images using deep learning.
- github: https://github.com/alexjc/neural-enhance
- docker: https://github.com/alexjc/neural-enhance/blob/master/docker-cpu.df
Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution
- intro: Digital Media & Communications R&D Center, Samsung Electronics, Seoul, Korea
- arxiv: https://arxiv.org/abs/1612.00085
EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis
- arxiv: https://arxiv.org/abs/1612.07919
Learning a Mixture of Deep Networks for Single Image Super-Resolution
- project page: http://www.ifp.illinois.edu/~dingliu2/accv2016/
- arxiv: https://arxiv.org/abs/1701.00823
- code: http://www.ifp.illinois.edu/~dingliu2/accv2016/codes/python_accv2016.zip
Dual Recovery Network with Online Compensation for Image Super-Resolution
- arxiv: https://arxiv.org/abs/1701.05652
Super-resolution Using Constrained Deep Texture Synthesis
- intro: Brown University & Georgia Institute of Technology
- arxiv: https://arxiv.org/abs/1701.07604
Pixel Recursive Super Resolution
- arxiv: https://arxiv.org/abs/1702.00783
- github(Tensorflow): https://github.com/nilboy/pixel-recursive-super-resolution
GUN: Gradual Upsampling Network for single image super-resolution
- arxiv: https://arxiv.org/abs/1703.04244
Single Image Super-resolution with a Parameter Economic Residual-like Convolutional Neural Network
- intro: Extentions of mmm 2017 paper
- arxiv: https://arxiv.org/abs/1703.08173
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
- intro: CVPR 2017
- project page(code+dataset): http://vllab1.ucmerced.edu/~wlai24/LapSRN/
- arxiv: https://arxiv.org/abs/1704.03915
- github(Matlab+MatConvNet): https://github.com/phoenix104104/LapSRN
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
- project page: http://vllab.ucmerced.edu/wlai24/LapSRN/
- arxiv: https://arxiv.org/abs/1710.01992
- github: https://github.com/phoenix104104/LapSRN
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
- intro: South China University of Technology
- arxiv: https://arxiv.org/abs/1705.05084
Super-Resolution via Deep Learning
- intro: COMSATS Institute of IT (CIIT)
- arxiv: https://arxiv.org/abs/1706.09077
High-Quality Face Image SR Using Conditional Generative Adversarial Networks
https://arxiv.org/abs/1707.00737
Enhanced Deep Residual Networks for Single Image Super-Resolution
- intro: CVPR 2017 workshop. Best paper award of the NTIRE2017 workshop, and the winners of the NTIRE2017 Challenge on Single Image Super-Resolution
- arxiv: https://arxiv.org/abs/1707.02921
- paper: http://cv.snu.ac.kr/publication/conf/2017/EDSR_fixed.pdf
- github: https://github.com/LimBee/NTIRE2017
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
- arxiv: https://arxiv.org/abs/1707.05425
- github(Tensorflow): https://github.com/jiny2001/dcscn-super-resolution
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module
- intro: ICIP 2017
- arxiv: https://arxiv.org/abs/1707.07128
Attention-Aware Face Hallucination via Deep Reinforcement Learning
https://arxiv.org/abs/1708.03132
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
https://arxiv.org/abs/1709.06229
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution
- intro: Chongqing University
- arxiv: https://arxiv.org/abs/1711.05431
D-PCN: Parallel Convolutional Neural Networks for Image Recognition in Reverse Adversarial Style
{https://arxiv.org/abs/1711.04237}(https://arxiv.org/abs/1711.04237)
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
- intro: IEEE Winter Conf. on Applications of Computer Vision (WACV) 2018, Lake Tahoe, USA
- arxiv: https://arxiv.org/abs/1711.04048
Video Super-resolution
Detail-revealing Deep Video Super-resolution
- arxiv: https://arxiv.org/abs/1704.02738
- github: https://github.com/jiangsutx/SPMC_VideoSR
End-to-End Learning of Video Super-Resolution with Motion Compensation
- intro: GCPR 2017
- arxiv: https://arxiv.org/abs/1707.00471
Image Denoising
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- arxiv: http://arxiv.org/abs/1608.03981
- github: https://github.com/cszn/DnCNN
Medical image denoising using convolutional denoising autoencoders
- arxiv: http://arxiv.org/abs/1608.04667
Rectifier Neural Network with a Dual-Pathway Architecture for Image Denoising
- arxiv: http://arxiv.org/abs/1609.03024
Non-Local Color Image Denoising with Convolutional Neural Networks
- arxiv: https://arxiv.org/abs/1611.06757
Joint Visual Denoising and Classification using Deep Learning
- intro: ICIP 2016
- arxiv: https://arxiv.org/abs/1612.01075
- github: https://github.com/ganggit/jointmodel
Deep Convolutional Denoising of Low-Light Images
- arxiv: https://arxiv.org/abs/1701.01687
Deep Class Aware Denoising
- arxiv: https://arxiv.org/abs/1701.01698
End-to-End Learning for Structured Prediction Energy Networks
- intro: University of Massachusetts & CMU
- arxiv: https://arxiv.org/abs/1703.05667
Block-Matching Convolutional Neural Network for Image Denoising
https://arxiv.org/abs/1704.00524
When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
https://arxiv.org/abs/1706.04284
Wide Inference Network for Image Denoising
https://arxiv.org/abs/1707.05414
Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising
- arxiv: https://arxiv.org/abs/1707.09135
- github(MatConvNet): https://github.com/cswin/WIN
Image Denoising via CNNs: An Adversarial Approach
- intro: Indian Institute of Science
- arxiv: https://arxiv.org/abs/1708.00159
An ELU Network with Total Variation for Image Denoising
- intro: 24th International Conference on Neural Information Processing (2017)
- arxiv: https://arxiv.org/abs/1708.04317
Dilated Residual Network for Image Denoising
https://arxiv.org/abs/1708.05473
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
- arxiv: https://arxiv.org/abs/1710.04026
- github(MatConvNet): https://github.com/cszn/FFDNet
Universal Denoising Networks : A Novel CNN-based Network Architecture for Image Denoising
https://arxiv.org/abs/1711.07807
Image Haze Removal
DehazeNet: An End-to-End System for Single Image Haze Removal
- arxiv: http://arxiv.org/abs/1601.07661
An All-in-One Network for Dehazing and Beyond
- intro: All-in-One Dehazing Network (AOD-Net)
- arxiv: https://arxiv.org/abs/1707.06543
Joint Transmission Map Estimation and Dehazing using Deep Networks
https://arxiv.org/abs/1708.00581
End-to-End United Video Dehazing and Detection
https://arxiv.org/abs/1709.03919
Image Dehazing using Bilinear Composition Loss Function
https://arxiv.org/abs/1710.00279
Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond
https://arxiv.org/abs/1711.06787
Image Rain Removal / De-raining
Clearing the Skies: A deep network architecture for single-image rain removal
- intro: DerainNet
- project page: http://smartdsp.xmu.edu.cn/derainNet.html
- arxiv: http://arxiv.org/abs/1609.02087
- code(Matlab): http://smartdsp.xmu.edu.cn/memberpdf/fuxueyang/derainNet/code.zip
Joint Rain Detection and Removal via Iterative Region Dependent Multi-Task Learning
- arxiv: http://arxiv.org/abs/1609.07769
Image De-raining Using a Conditional Generative Adversarial Network
- arxiv: https://arxiv.org/abs/1701.05957
Fence Removal
Deep learning based fence segmentation and removal from an image using a video sequence
- intro: ECCV Workshop on Video Segmentation, 2016
- arxiv: http://arxiv.org/abs/1609.07727
Snow Removal
DesnowNet: Context-Aware Deep Network for Snow Removal
https://arxiv.org/abs/1708.04512
Blur Detection and Removal
Learning to Deblur
- arxiv: http://arxiv.org/abs/1406.7444
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
- arxiv: http://arxiv.org/abs/1503.00593
End-to-End Learning for Image Burst Deblurring
- arxiv: http://arxiv.org/abs/1607.04433
Deep Video Deblurring
- intro: CVPR 2017 spotlight paper
- project page(code+dataset): http://www.cs.ubc.ca/labs/imager/tr/2017/DeepVideoDeblurring/
- arxiv: https://arxiv.org/abs/1611.08387 https://github.com/shuochsu/DeepVideoDeblurring
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
- arxiv: https://arxiv.org/abs/1612.02177
- github(official. Torch)): https://github.com/SeungjunNah/DeepDeblur_release
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
- arxiv: https://arxiv.org/abs/1612.02583
Motion Deblurring in the Wild
- arxiv: https://arxiv.org/abs/1701.01486
Deep Face Deblurring
https://arxiv.org/abs/1704.08772
Learning Blind Motion Deblurring
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1708.04208
Deep Generative Filter for Motion Deblurring
https://arxiv.org/abs/1709.03481
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
- arxiv: https://arxiv.org/abs/1711.07064
- github: https://github.com/KupynOrest/DeblurGAN
Image Compression
An image compression and encryption scheme based on deep learning
- arxiv: http://arxiv.org/abs/1608.05001
Full Resolution Image Compression with Recurrent Neural Networks
- arxiv: http://arxiv.org/abs/1608.05148
- github: https://github.com/tensorflow/models/tree/master/compression
Image Compression with Neural Networks
- blog: https://research.googleblog.com/2016/09/image-compression-with-neural-networks.html
Lossy Image Compression With Compressive Autoencoders
- paper: http://openreview.net/pdf?id=rJiNwv9gg
- review: http://qz.com/835569/twitter-is-getting-close-to-making-all-your-pictures-just-a-little-bit-smaller/
End-to-end Optimized Image Compression
- arxiv: https://arxiv.org/abs/1611.01704
- notes: https://blog.acolyer.org/2017/05/08/end-to-end-optimized-image-compression/
CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression
- arxiv: https://arxiv.org/abs/1611.07233
Semantic Perceptual Image Compression using Deep Convolution Networks
- intro: Accepted to Data Compression Conference
- intro: Semantic JPEG image compression using deep convolutional neural network (CNN)
- arxiv: https://arxiv.org/abs/1612.08712
- github: https://github.com/iamaaditya/image-compression-cnn
Generative Compression
- intro: MIT
- arxiv: https://arxiv.org/abs/1703.01467
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
https://arxiv.org/abs/1703.10114
Learning Convolutional Networks for Content-weighted Image Compression
https://arxiv.org/abs/1703.10553
Real-Time Adaptive Image Compression
- intro: ICML 2017
- keywords: GAN
- project page: http://www.wave.one/icml2017
- arxiv: https://arxiv.org/abs/1705.05823
Image Quality Assessment
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
- arxiv: https://arxiv.org/abs/1612.01697
Image Matting
Deep Image Matting
- intro: CVPR 2017
- intro: Beckman Institute for Advanced Science and Technology & Adobe Research
- arxiv: https://arxiv.org/abs/1703.03872
Fast Deep Matting for Portrait Animation on Mobile Phone
- intro: ACM Multimedia Conference (MM) 2017
- intro: does not need any interaction and can realize real-time matting with 15 fps
- arxiv: https://arxiv.org/abs/1707.08289
Image Blending
GP-GAN: Towards Realistic High-Resolution Image Blending
- project page: https://wuhuikai.github.io/GP-GAN-Project/
- arxiv: https://arxiv.org/abs/1703.07195
- github(Official, Chainer): https://github.com/wuhuikai/GP-GAN
Image Enhancement
Deep Bilateral Learning for Real-Time Image Enhancement
- intro: MIT & Google Research
- arxiv: https://arxiv.org/abs/1707.02880
Aesthetic-Driven Image Enhancement by Adversarial Learning
- intro: CUHK
- arxiv: https://arxiv.org/abs/1707.05251
Abnormality Detection / Anomaly Detection
Toward a Taxonomy and Computational Models of Abnormalities in Images
- arxiv: http://arxiv.org/abs/1512.01325
Depth Prediction / Depth Estimation
Deep Convolutional Neural Fields for Depth Estimation from a Single Image
- intro: CVPR 2015
- arxiv: https://arxiv.org/abs/1411.6387
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
- intro: IEEE T. Pattern Analysis and Machine Intelligence
- arxiv: https://arxiv.org/abs/1502.07411
- bitbucket: https://bitbucket.org/fayao/dcnf-fcsp
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
- intro: ECCV 2016
- arxiv: https://arxiv.org/abs/1603.04992
- github: https://github.com/Ravi-Garg/Unsupervised_Depth_Estimation
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
- intro: NIPS 2016
- project pag: http://ttic.uchicago.edu/~ayanc/mdepth/
- arxiv: http://arxiv.org/abs/1605.07081
- github: https://github.com/ayanc/mdepth/
Deeper Depth Prediction with Fully Convolutional Residual Networks
- arxiv: https://arxiv.org/abs/1606.00373
- github: https://github.com/iro-cp/FCRN-DepthPrediction
Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference
https://arxiv.org/abs/1705.00534
Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference
- intro: Northwestern Polytechnical University
- arxiv: https://arxiv.org/abs/1708.02287
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
- arxiv: https://arxiv.org/abs/1709.07492
- video: https://www.youtube.com/watch?v=vNIIT_M7x7Y
- github: https://github.com/fangchangma/sparse-to-dense
Texture Synthesis
Texture Synthesis Using Convolutional Neural Networks
- arxiv: http://arxiv.org/abs/1505.07376
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
- intro: IMCL 2016
- arxiv: http://arxiv.org/abs/1603.03417
- github: https://github.com/DmitryUlyanov/texture_nets
- notes: https://blog.acolyer.org/2016/09/23/texture-networks-feed-forward-synthesis-of-textures-and-stylized-images/
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
- arxiv: http://arxiv.org/abs/1604.04382
- github(Torch): https://github.com/chuanli11/MGANs
Texture Synthesis with Spatial Generative Adversarial Networks
- arxiv: https://arxiv.org/abs/1611.08207
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
- intro: Skolkovo Institute of Science and Technology & Yandex & University of Oxford
- arxiv: https://arxiv.org/abs/1701.02096
Deep TEN: Texture Encoding Network
- intro: CVPR 2017
- project page: http://zhanghang1989.github.io/DeepEncoding/
- arxiv: https://arxiv.org/abs/1612.02844
- github: https://github.com/zhanghang1989/Deep-Encoding
- notes: https://zhuanlan.zhihu.com/p/25013378
Diversified Texture Synthesis with Feed-forward Networks
- intro: CVPR 2017. University of California & Adobe Research
- arxiv: https://arxiv.org/abs/1703.01664
- github: https://github.com/Yijunmaverick/MultiTextureSynthesis
Image Cropping
A2-RL: Aesthetics Aware Reinforcement Learning for Automatic Image Cropping
https://arxiv.org/abs/1709.04595
Deep Cropping via Attention Box Prediction and Aesthetics Assessment
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1710.08014
Image Synthesis
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
- arxiv: http://arxiv.org/abs/1601.04589
Generative Adversarial Text to Image Synthesis
- intro: ICML 2016
- arxiv: http://arxiv.org/abs/1605.05396
- github(Tensorflow): https://github.com/paarthneekhara/text-to-image
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
- intro: Rutgers University & Lehigh University & The Chinese University of Hong Kong & University of North Carolina at Charlotte
- arxiv: https://arxiv.org/abs/1612.03242
- github: https://github.com/hanzhanggit/StackGAN
- github: https://github.com/brangerbriz/docker-StackGAN
Image Tagging
Fast Zero-Shot Image Tagging
- project: http://crcv.ucf.edu/projects/fastzeroshot/
Flexible Image Tagging with Fast0Tag
- blog: https://gab41.lab41.org/flexible-image-tagging-with-fast0tag-681c6283c9b7
Sampled Image Tagging and Retrieval Methods on User Generated Content
- arxiv: https://arxiv.org/abs/1611.06962
- github: https://github.com/lab41/attalos
Kill Two Birds with One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement
- intro: AAAI 2018
- arxiv: https://arxiv.org/abs/1711.06998
Image Matching
Learning Fine-grained Image Similarity with Deep Ranking
- intro: CVPR 2014
- intro: Triplet Sampling
- arxiv: http://arxiv.org/abs/1404.4661
Learning to compare image patches via convolutional neural networks
- intro: CVPR 2015. siamese network
- project page: http://imagine.enpc.fr/~zagoruys/deepcompare.html
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zagoruyko_Learning_to_Compare_2015_CVPR_paper.pdf
- github: https://github.com/szagoruyko/cvpr15deepcompare
MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching
- intro: CVPR 2015. siamese network
- paper: http://www.cs.unc.edu/~xufeng/cs/papers/cvpr15-matchnet.pdf
- extended abstract: http://www.cv-foundation.org/openaccess/content_cvpr_2015/ext/2A_114_ext.pdf
- github: https://github.com/hanxf/matchnet
Fashion Style in 128 Floats
- intro: CVPR 2016. StyleNet
- project page: http://hi.cs.waseda.ac.jp/~esimo/en/research/stylenet/
- paper: http://hi.cs.waseda.ac.jp/~esimo/publications/SimoSerraCVPR2016.pdf
- github: https://github.com/bobbens/cvpr2016_stylenet
Fully-Trainable Deep Matching
- intro: BMVC 2016
- project page: http://lear.inrialpes.fr/src/deepmatching/
- arxiv: http://arxiv.org/abs/1609.03532
Local Similarity-Aware Deep Feature Embedding
- intro: NIPS 2016
- arxiv: https://arxiv.org/abs/1610.08904
Convolutional neural network architecture for geometric matching
- intro: CVPR 2017. Inria
- project page: http://www.di.ens.fr/willow/research/cnngeometric/
- arxiv: https://arxiv.org/abs/1703.05593
- github: https://github.com/ignacio-rocco/cnngeometric_matconvnet
Multi-Image Semantic Matching by Mining Consistent Features
https://arxiv.org/abs/1711.07641
Image Editing
Neural Photo Editing with Introspective Adversarial Networks
- intro: Heriot-Watt University
- arxiv: http://arxiv.org/abs/1609.07093
- github: https://github.com/ajbrock/Neural-Photo-Editor
Deep Feature Interpolation for Image Content Changes
- intro: CVPR 2017. Cornell University & Washington University
- arxiv: https://arxiv.org/abs/1611.05507
- github(official): https://github.com/paulu/deepfeatinterp
- github: https://github.com/slang03/dfi-tensorflow
Invertible Conditional GANs for image editing
- intro: NIPS 2016 Workshop on Adversarial Training
- arxiv: https://arxiv.org/abs/1611.06355
- github: https://github.com/Guim3/IcGAN
Semantic Facial Expression Editing using Autoencoded Flow
- intro: University of Illinois at Urbana-Champaign & The Chinese University of Hong Kong & Google
- arxiv: https://arxiv.org/abs/1611.09961
Language-Based Image Editing with Recurrent Attentive Models
https://arxiv.org/abs/1711.06288
Face Swap
Fast Face-swap Using Convolutional Neural Networks
- intro: Ghent University & Twitter
- arxiv: https://arxiv.org/abs/1611.09577
Face Editing
Neural Face Editing with Intrinsic Image Disentangling
- intro: CVPR 2017 oral
- project page: http://www3.cs.stonybrook.edu/~cvl/content/neuralface/neuralface.html
- arxiv: https://arxiv.org/abs/1704.04131
Deep Learning for Makeup
Makeup like a superstar: Deep Localized Makeup Transfer Network
- intro: IJCAI 2016
- arxiv: http://arxiv.org/abs/1604.07102
Makeup-Go: Blind Reversion of Portrait Edit
- intro: The Chinese University of Hong Kong & Tencent Youtu Lab
- paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Makeup-Go_Blind_Reversion_ICCV_2017_paper.pdf
- paper: http://open.youtu.qq.com/content/file/iccv17_makeupgo.pdf
Music Tagging
Automatic tagging using deep convolutional neural networks
- arxiv: https://arxiv.org/abs/1606.00298
- github: https://github.com/keunwoochoi/music-auto_tagging-keras
Music tagging and feature extraction with MusicTaggerCRNN
https://keras.io/applications/#music-tagging-and-feature-extraction-with-musictaggercrnn
Action Recognition
Single Image Action Recognition by Predicting Space-Time Saliency
https://arxiv.org/abs/1705.04641
Attentional Pooling for Action Recognition
- intro: NIPS 2017
- project page: https://rohitgirdhar.github.io/AttentionalPoolingAction/
- arxiv: https://arxiv.org/abs/1711.01467
- github: https://github.com/rohitgirdhar/AttentionalPoolingAction/
CTR Prediction
Deep CTR Prediction in Display Advertising
- intro: ACM Multimedia Conference 2016
- arxiv: https://arxiv.org/abs/1609.06018
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
- intro: Harbin Institute of Technology & Huawei
- arxiv: https://arxiv.org/abs/1703.04247
Deep Interest Network for Click-Through Rate Prediction
- intro: Alibaba Inc.
- arxiv: https://arxiv.org/abs/1706.06978
Image Matters: Jointly Train Advertising CTR Model with Image Representation of Ad and User Behavior
- intro: Alibaba Inc.
- arxiv: https://arxiv.org/abs/1711.06505
Cryptography
Learning to Protect Communications with Adversarial Neural Cryptography
- intro: Google Brain
- arxiv: https://arxiv.org/abs/1610.06918
- github(Theano): https://github.com/nlml/adversarial-neural-crypt
- github(TensorFlow): https://github.com/ankeshanand/neural-cryptography-tensorflow
Adversarial Neural Cryptography in Theano
- blog: https://nlml.github.io/neural-networks/adversarial-neural-cryptography/
Embedding Watermarks into Deep Neural Networks
- arxiv: https://arxiv.org/abs/1701.04082
- github: https://github.com/yu4u/dnn-watermark
Cyber Security
Collection of Deep Learning Cyber Security Research Papers
- blog: https://medium.com/@jason_trost/collection-of-deep-learning-cyber-security-research-papers-e1f856f71042#.fcus2cu9m
Lip Reading
LipNet: Sentence-level Lipreading
LipNet: End-to-End Sentence-level Lipreading
- arxiv: https://arxiv.org/abs/1611.01599
- paper: http://openreview.net/pdf?id=BkjLkSqxg
- github: https://github.com/bshillingford/LipNet
Lip Reading Sentences in the Wild
- intro: University of Oxford & Google DeepMind
- arxiv: https://arxiv.org/abs/1611.05358
- youtube: https://www.youtube.com/watch?v=5aogzAUPilE
Combining Residual Networks with LSTMs for Lipreading
- arxiv: https://arxiv.org/abs/1703.04105
End-to-End Multi-View Lipreading
- intro: BMVC 2017
- arxiv: https://arxiv.org/abs/1709.00443
Event Recognition
Better Exploiting OS-CNNs for Better Event Recognition in Images
- arxiv: http://arxiv.org/abs/1510.03979
Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images
- arxiv: http://arxiv.org/abs/1609.00162
IOD-CNN: Integrating Object Detection Networks for Event Recognition
https://arxiv.org/abs/1703.07431
Others
Selfai: Predicting Facial Beauty in Selfies
Selfai: A Method for Understanding Beauty in Selfies
- blog: http://www.erogol.com/selfai-predicting-facial-beauty-selfies/
- github: https://github.com/erogol/beauty.torch
Deep Learning Enables You to Hide Screen when Your Boss is Approaching
- blog: http://ahogrammer.com/2016/11/15/deep-learning-enables-you-to-hide-screen-when-your-boss-is-approaching/
- github: https://github.com/Hironsan/BossSensor
Blogs
40 Ways Deep Learning is Eating the World
https://medium.com/intuitionmachine/the-ultimate-deep-learning-applications-list-434d1425da1d#.rxq8xvbfz
Applications
http://www.deeplearningpatterns.com/doku.php/applications
Systematic Approach To Applications Of Deep Learning
https://gettocode.com/2016/11/25/systematic-approach-to-applications-of-deep-learning/
Resources
Deep Learning Gallery - a curated collection of deep learning projects
http://deeplearninggallery.com/