视频研究入门经典
Labor-Free Video Concept Learningby Jointly Exploiting Web Videos and Images
intro: CVPR 2016
intro: Lead–Exceed Neural Network (LENN), LSTM
paper: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/CVPR16_webly_final.pdf
Video Fill in the Blank with Merging LSTMs
intro: for Large Scale Movie Description and Understanding Challenge (LSMDC) 2016, "Movie fill-in-the-blank" Challenge, UCF_CRCV
intro: Video-Fill-in-the-Blank (ViFitB)
arxiv: https://arxiv.org/abs/1610.04062
Video Pixel Networks
intro: Google DeepMind
arxiv: https://arxiv.org/abs/1610.00527
Robust Video Synchronization using Unsupervised Deep Learning
arxiv: https://arxiv.org/abs/1610.05985
Video Propagation Networks
intro: CVPR 2017. Max Planck Institute for Intelligent Systems & Bernstein Center for Computational Neuroscience
project page: https://varunjampani.github.io/vpn/
arxiv: https://arxiv.org/abs/1612.05478
github(Caffe): https://github.com/varunjampani/video_prop_networks
Video Frame Synthesis using Deep Voxel Flow
project page: https://liuziwei7.github.io/projects/VoxelFlow.html
arxiv: https://arxiv.org/abs/1702.02463
Optimizing Deep CNN-Based Queries over Video Streams at Scale
intro: Stanford InfoLab
keywords: NoScope. difference detectors, specialized models
arxiv: https://arxiv.org/abs/1703.02529
github: https://github.com/stanford-futuredata/noscope
github: https://github.com/stanford-futuredata/tensorflow-noscope
NoScope: 1000x Faster Deep Learning Queries over Video
http://dawn.cs.stanford.edu/2017/06/22/noscope/
Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos
intro: CVPR 2017. Stanford University & University of Southern California
arxiv: https://arxiv.org/abs/1703.02521
ProcNets: Learning to Segment Procedures in Untrimmed and Unconstrained Videos
https://arxiv.org/abs/1703.09788
Unsupervised Learning Layers for Video Analysis
intro: Baidu Research
intro: "The experiments demonstrated the potential applications of UL layers and online learning algorithm to head orientation estimation and moving object localization"
arxiv: https://arxiv.org/abs/1705.08918
Look, Listen and Learn
intro: DeepMind
intro: "Audio-Visual Correspondence" learning
arxiv: https://arxiv.org/abs/1705.08168
Video Imagination from a Single Image with Transformation Generation
intro: Peking University
arxiv: https://arxiv.org/abs/1706.04124
github: https://github.com/gitpub327/VideoImagination
Learning to Learn from Noisy Web Videos
intro: CVPR 2017. Stanford University & CMU & Simon Fraser University
arxiv: https://arxiv.org/abs/1706.02884
Convolutional Long Short-Term Memory Networks for Recognizing First Person Interactions
intro: Accepted on the second International Workshop on Egocentric Perception, Interaction and Computing(EPIC) at International Conference on Computer Vision(ICCV-17)
arxiv: https://arxiv.org/abs/1709.06495
Learning Binary Residual Representations for Domain-specific Video Streaming
intro: AAAI 2018
project page: http://research.nvidia.com/publication/2018-02_Learning-Binary-Residual
arxiv: https://arxiv.org/abs/1712.05087
Video Representation Learning Using Discriminative Pooling
intro: CVPR 2018
arxiv: https://arxiv.org/abs/1803.10628
Rethinking the Faster R-CNN Architecture for Temporal Action Localization
intro: CVPR 2018
arxiv: https://arxiv.org/abs/1804.07667
Deep Keyframe Detection in Human Action Videos
intro: two-stream ConvNet
arxiv: https://arxiv.org/abs/1804.10021
FFNet: Video Fast-Forwarding via Reinforcement Learning
intro: CVPR 2018
arxiv: https://arxiv.org/abs/1805.02792
Fast forwarding Egocentric Videos by Listening and Watching
https://arxiv.org/abs/1806.04620
Scanner: Efficient Video Analysis at Scale
intro: CMU
arxiv: https://arxiv.org/abs/1805.07339
Massively Parallel Video Networks
intro: DeepMind & University of Oxford
arxiv: https://arxiv.org/abs/1806.03863
Object Level Visual Reasoning in Videos
intro: LIRIS & Facebook AI Research
arxiv: https://arxiv.org/abs/1806.06157
Video Time: Properties, Encoders and Evaluation
intro: BMVC 2018
arxiv: https://arxiv.org/abs/1807.06980
视频分类
Large-scale Video Classification with Convolutional Neural Networks
intro: CVPR 2014
project page: http://cs.stanford.edu/people/karpathy/deepvideo/
paper: www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Karpathy_Large-scale_Video_Classification_2014_CVPR_paper.pdf
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
intro: Video-level event detection. extracting deep features for each frame, averaging frame-level deep features
arxiv: http://arxiv.org/abs/1503.04144
Beyond Short Snippets: Deep Networks for Video Classification
intro: CNN + LSTM
arxiv: http://arxiv.org/abs/1503.08909
demo: http://pan.baidu.com/s/1eQ9zLZk
Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification
intro: ACM Multimedia, 2015
arxiv: http://arxiv.org/abs/1504.01561
Video Content Recognition with Deep Learning
author: Zuxuan Wu, Fudan University
slides: http://vision.ouc.edu.cn/valse/slides/20160420/Zuxuan%20Wu%20-%20Video%20Content%20Recognition%20with%20Deep%20Learning-Zuxuan%20Wu.pdf
Video Content Recognition with Deep Learning
author: Yu-Gang Jiang, Lab for Big Video Data Analytics (BigVid), Fudan University
slides: http://www.yugangjiang.info/slides/DeepVideoTalk-2015.pdf
Efficient Large Scale Video Classification
intro: Google
arxiv: http://arxiv.org/abs/1505.06250
Fusing Multi-Stream Deep Networks for Video Classification
arxiv: http://arxiv.org/abs/1509.06086
Learning End-to-end Video Classification with Rank-Pooling
paper: http://jmlr.org/proceedings/papers/v48/fernando16.html
paper: http://jmlr.csail.mit.edu/proceedings/papers/v48/fernando16.pdf
summary(by Hugo Larochelle): http://www.shortscience.org/paper?bibtexKey=conf/icml/FernandoG16#hlarochelle
Deep Learning for Video Classification and Captioning
arxiv: http://arxiv.org/abs/1609.06782
Fast Video Classification via Adaptive Cascading of Deep Models
arxiv: https://arxiv.org/abs/1611.06453
Deep Feature Flow for Video Recognition
intro: CVPR 2017
intro: It provides a simple, fast, accurate, and end-to-end framework for video recognition (e.g., object detection and semantic segmentation in videos)
arxiv: https://arxiv.org/abs/1611.07715
github(official, MXNet): https://github.com/msracver/Deep-Feature-Flow
youtube: https://www.youtube.com/watch?v=J0rMHE6ehGw
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks
https://arxiv.org/abs/1706.04488
Deep Learning Methods for Efficient Large Scale Video Labeling
intro: Solution to the Kaggle's competition Google Cloud & YouTube-8M Video Understanding Challenge
arxiv: https://arxiv.org/abs/1706.04572
github: https://github.com/mpekalski/Y8M
Learnable pooling with Context Gating for video classification
intro: CVPR17 Youtube 8M workshop. Kaggle 1st place
arxiv: https://arxiv.org/abs/1706.06905
github: https://github.com/antoine77340/LOUPE
Aggregating Frame-level Features for Large-Scale Video Classification
intro: Youtube-8M Challenge, 4th place
arxiv: https://arxiv.org/abs/1707.00803
Tensor-Train Recurrent Neural Networks for Video Classification
https://arxiv.org/abs/1707.01786
Hierarchical Deep Recurrent Architecture for Video Understanding
intro: Classification Challenge Track paper in CVPR 2017 Workshop on YouTube-8M Large-Scale Video Understanding
arxiv: https://arxiv.org/abs/1707.03296
Large-scale Video Classification guided by Batch Normalized LSTM Translator
intro: CVPR2017 Workshop on Youtube-8M Large-scale Video Understanding
arxiv: https://arxiv.org/abs/1707.04045
UTS submission to Google YouTube-8M Challenge 2017
intro: CVPR'17 Workshop on YouTube-8M
arxiv: https://arxiv.org/abs/1707.04143
github: https://github.com/ffmpbgrnn/yt8m
A spatiotemporal model with visual attention for video classification
https://arxiv.org/abs/1707.02069
Cultivating DNN Diversity for Large Scale Video Labelling
intro: CVPR 2017 Youtube-8M Workshop
arxiv: https://arxiv.org/abs/1707.04272
Attention Transfer from Web Images for Video Recognition
intro: ACM Multimedia, 2017
arxiv: https://arxiv.org/abs/1708.00973
Non-local Neural Networks
intro: CVPR 2018. CMU & Facebook AI Research
arxiv: https://arxiv.org/abs/1711.07971
github(Caffe2): https://github.com/facebookresearch/video-nonlocal-net
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
https://arxiv.org/abs/1711.08200
Appearance-and-Relation Networks for Video Classification
arxiv: https://arxiv.org/abs/1711.09125
github: https://github.com/wanglimin/ARTNet
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
intro: ECCV 2018. Google Research & University of California San Diego
arxiv: https://arxiv.org/abs/1712.04851
Long Activity Video Understanding using Functional Object-Oriented Network
https://arxiv.org/abs/1807.00983
Deep Architectures and Ensembles for Semantic Video Classification
https://arxiv.org/abs/1807.01026
Deep Discriminative Model for Video Classification
intro: ECCV 2018
arxiv: https://arxiv.org/abs/1807.08259
Deep Video Color Propagation
intro: BMVC 2018
arxuv: https://arxiv.org/abs/1808.03232
Non-local NetVLAD Encoding for Video Classification
intro: ECCV 2018 workshop on YouTube-8M Large-Scale Video Understanding
intro: Tencent AI Lab & Fudan University
arxiv: https://arxiv.org/abs/1810.00207
Learnable Pooling Methods for Video Classification
intro: Youtube 8M ECCV18 Workshop
arxiv: https://arxiv.org/abs/1810.00530
github: https://github.com/pomonam/LearnablePoolingMethods
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification
intro: ECCV 2018 workshop
arxiv: https://arxiv.org/abs/1811.05014
github: https://github.com/linrongc/youtube-8m
视频行为识别 / 行为检测
3d convolutional neural networks for human action recognition
paper: http://www.cs.odu.edu/~sji/papers/pdf/Ji_ICML10.pdf
Sequential Deep Learning for Human Action Recognition
paper: http://liris.cnrs.fr/Documents/Liris-5228.pdf
Two-stream convolutional networks for action recognition in videos
arxiv: http://arxiv.org/abs/1406.2199
Finding action tubes
intro: "built action models from shape and motion cues. They start from the image proposals and select the motion salient subset of them and extract saptio-temporal features to represent the video using the CNNs."
arxiv: http://arxiv.org/abs/1411.6031
Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition
Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
intro: CVPR 2015. TDD
paper: www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Action_Recognition_With_2015_CVPR_paper.pdf
ext: http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/2B_105_ext.pdf
poster: https://wanglimin.github.io/papers/WangQT_CVPR15_Poster.pdf
github: https://github.com/wanglimin/TDD
Action Recognition by Hierarchical Mid-level Action Elements
paper: http://cvgl.stanford.edu/papers/tian2015.pdf
Contextual Action Recognition with R*CNN
arxiv: http://arxiv.org/abs/1505.01197
github: https://github.com/gkioxari/RstarCNN
Towards Good Practices for Very Deep Two-Stream ConvNets
arxiv: http://arxiv.org/abs/1507.02159
github: https://github.com/yjxiong/caffe
Action Recognition using Visual Attention
intro: LSTM / RNN
arxiv: http://arxiv.org/abs/1511.04119
project page: http://shikharsharma.com/projects/action-recognition-attention/
github(Python/Theano): https://github.com/kracwarlock/action-recognition-visual-attention
End-to-end Learning of Action Detection from Frame Glimpses in Videos
intro: CVPR 2016
project page: http://ai.stanford.edu/~syyeung/frameglimpses.html
arxiv: http://arxiv.org/abs/1511.06984
paper: http://vision.stanford.edu/pdf/yeung2016cvpr.pdf
Multi-velocity neural networks for gesture recognition in videos
arxiv: http://arxiv.org/abs/1603.06829
Active Learning for Online Recognition of Human Activities from Streaming Videos
arxiv: http://arxiv.org/abs/1604.02855
Convolutional Two-Stream Network Fusion for Video Action Recognition
arxiv: http://arxiv.org/abs/1604.06573
github: https://github.com/feichtenhofer/twostreamfusion
Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables
arxiv: http://arxiv.org/abs/1604.08880
Unsupervised Semantic Action Discovery from Video Collections
arxiv: http://arxiv.org/abs/1605.03324
Anticipating Visual Representations from Unlabeled Video
paper: http://web.mit.edu/vondrick/prediction.pdf
VideoLSTM Convolves, Attends and Flows for Action Recognition
arxiv: http://arxiv.org/abs/1607.01794
Hierarchical Attention Network for Action Recognition in Videos (HAN)
arxiv: http://arxiv.org/abs/1607.06416
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
arxiv: http://arxiv.org/abs/1607.07043
Connectionist Temporal Modeling for Weakly Supervised Action Labeling
arxiv: http://arxiv.org/abs/1607.08584
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016
intro: won the 1st place in the untrimmed video classification task of ActivityNet Challenge 2016. TSN
arxiv: http://arxiv.org/abs/1608.00797
github: https://github.com/yjxiong/anet2016-cuhk
Actionness Estimation Using Hybrid FCNs
intro: CVPR 2016. H-FCN
project page: http://wanglimin.github.io/actionness_hfcn/index.html
paper: http://wanglimin.github.io/papers/WangQTV_CVPR16.pdf
github: https://github.com/wanglimin/actionness-estimation/
Real-time Action Recognition with Enhanced Motion Vector CNNs
intro: CVPR 2016
project page: http://zbwglory.github.io/MV-CNN/index.html
paper: http://wanglimin.github.io/papers/ZhangWWQW_CVPR16.pdf
github: https://github.com/zbwglory/MV-release
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
intro: ECCV 2016. HMDB51: 69.4%, UCF101: 94.2%
arxiv: http://arxiv.org/abs/1608.00859
paper: http://wanglimin.github.io/papers/WangXWQLTV_ECCV16.pdf
github: https://github.com/yjxiong/temporal-segment-networks
Temporal Segment Networks for Action Recognition in Videos
intro: An extension of submission http://arxiv.org/abs/1608.00859
arxiv: https://arxiv.org/abs/1705.02953
Hierarchical Attention Network for Action Recognition in Videos
arxiv: http://arxiv.org/abs/1607.06416
DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns
intro: CVPR 2016
arxiv: http://arxiv.org/abs/1608.03217
Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition
arxiv: http://arxiv.org/abs/1608.04339
Dynamic Image Networks for Action Recognition
intro: CVPR 2016
arxiv: http://users.cecs.anu.edu.au/~sgould/papers/cvpr16-dynamic_images.pdf
github: https://github.com/hbilen/dynamic-image-nets
Human Action Recognition without Human
arxiv: http://arxiv.org/abs/1608.07876
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
arxiv: http://arxiv.org/abs/1608.08242
ECCV 2016 workshop: http://bravenewmotion.github.io/
Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks
intro: Bachelor Thesis Report at ETSETB TelecomBCN
project page: https://imatge-upc.github.io/activitynet-2016-cvprw/
arxiv: http://arxiv.org/abs/1608.08128
github: https://github.com/imatge-upc/activitynet-2016-cvprw
Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN
arxiv: http://arxiv.org/abs/1609.03056
Semi-Coupled Two-Stream Fusion ConvNets for Action Recognition at Extremely Low Resolutions
arxiv: https://arxiv.org/abs/1610.03898
Spatiotemporal Residual Networks for Video Action Recognition
intro: NIPS 2016
arxiv: https://arxiv.org/abs/1611.02155
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
arxiv: https://arxiv.org/abs/1611.02447
Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput
arxiv: https://arxiv.org/abs/1611.03607
Joint Network based Attention for Action Recognition
arxiv: https://arxiv.org/abs/1611.05215
Temporal Convolutional Networks for Action Segmentation and Detection
arxiv: https://arxiv.org/abs/1611.05267
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos
arxiv: https://arxiv.org/abs/1611.08240
ActionFlowNet: Learning Motion Representation for Action Recognition
arxiv: https://arxiv.org/abs/1612.03052
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition
intro: Australian Center for Robotic Vision & Data61/CSIRO
arxiv: https://arxiv.org/abs/1701.05432
Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos
https://arxiv.org/abs/1703.10664
Temporal Action Detection with Structured Segment Networks
project page: http://yjxiong.me/others/ssn/
arxiv: https://arxiv.org/abs/1704.06228
github: https://github.com/yjxiong/action-detection
Recurrent Residual Learning for Action Recognition
https://arxiv.org/abs/1706.08807
Hierarchical Multi-scale Attention Networks for Action Recognition
https://arxiv.org/abs/1708.07590
Two-stream Flow-guided Convolutional Attention Networks for Action Recognition
intro: International Conference of Computer Vision Workshop (ICCVW), 2017
arxiv: https://arxiv.org/abs/1708.09268
Action Classification and Highlighting in Videos
https://arxiv.org/abs/1708.09522
Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN
https://arxiv.org/abs/1710.03383
End-to-end Video-level Representation Learning for Action Recognition
keywords: Deep networks with Temporal Pyramid Pooling (DTPP)
arxiv: https://arxiv.org/abs/1711.04161
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition
intro: WACV 2018
arxiv: https://arxiv.org/abs/1801.03983
DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks
https://arxiv.org/abs/1801.07230
A Fusion of Appearance based CNNs and Temporal evolution of Skeleton with LSTM for Daily Living Action Recognition
https://arxiv.org/abs/1802.00421
Real-Time End-to-End Action Detection with Two-Stream Networks
https://arxiv.org/abs/1802.08362
A Closer Look at Spatiotemporal Convolutions for Action Recognition
intro: CVPR 2018. Facebook Research
intro: R(2+1)D and Mixed-Convolutions for Action Recognition.
project page: https://dutran.github.io/R2Plus1D/
arxiv: https://arxiv.org/abs/1711.11248
github: https://github.com/facebookresearch/R2Plus1D
VideoCapsuleNet: A Simplified Network for Action Detection
https://arxiv.org/abs/1805.08162
Where and When to Look? Spatio-temporal Attention for Action Recognition in Videos
https://arxiv.org/abs/1810.04511
Projects
A Torch Library for Action Recognition and Detection Using CNNs and LSTMs
intro: CS231n student project report
paper: http://cs231n.stanford.edu/reports2016/221_Report.pdf
github: https://github.com/garythung/torch-lrcn
2016 ActivityNet action recognition challenge. CNN + LSTM approach. Multi-threaded loading.
github: https://github.com/jrbtaylor/ActivityNet
LSTM for Human Activity Recognition
github: https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/
github(MXNet): https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/HumanActivityRecognition
Scanner: Efficient Video Analysis at Scale
intro: Locate and recognize faces in a video, Detect shots in a film, Search videos by image
github: https://github.com/scanner-research/scanner
Charades Starter Code for Activity Classification and Localization
intro: Activity Recognition Algorithms for the Charades Dataset
github: https://github.com/gsig/charades-algorithms
NonLocalNetwork and Sequeeze-Excitation Network
intro: MXNet implementation of Non-Local and Squeeze-Excitation network
github: https://github.com/WillSuen/NonLocalandSEnet
事件识别
TagBook: A Semantic Video Representation without Supervision for Event Detection
arxiv: http://arxiv.org/abs/1510.02899
AENet: Learning Deep Audio Features for Video Analysis
arxiv: https://arxiv.org/abs/1701.00599
github: https://github.com/znaoya/aenet
事件检测
DevNet: A Deep Event Network for Multimedia Event Detection and Evidence Recounting
paper: http://120.52.72.47/winsty.net/c3pr90ntcsf0/papers/devnet.pdf
paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Gan_DevNet_A_Deep_2015_CVPR_paper.pdf
Detecting events and key actors in multi-person videos
intro: CVPR 2016
arxiv: http://arxiv.org/abs/1511.02917
paper: www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Ramanathan_Detecting_Events_and_CVPR_2016_paper.pdf
paper: http://vision.stanford.edu/pdf/johnson2016cvpr.pdf
blog: http://www.leiphone.com/news/201606/l1TKIRFLO3DUFNNu.html
Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection
intro: INTERSPEECH 2016
arxiv: https://arxiv.org/abs/1604.07160
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning
arxiv: https://arxiv.org/abs/1612.07403
Joint Event Detection and Description in Continuous Video Streams
intro: Joint Event Detection and Description Network (JEDDi-Net)
arxiv: https://arxiv.org/abs/1802.10250
转自: https://blog.csdn.net/WJ_MeiMei/article/details/84344836