深度学习之异常检测+视频预测+预训练+视频标注+镜头边界检测+行为分割+视频生成gif--附带源码和作者主页

女爷i 2022-04-15 06:42 163阅读 0赞

# 异常检测 #

### **Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes** ###

*  arxiv: [http://arxiv.org/abs/1609.00866][http_arxiv.org_abs_1609.00866]

### **Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks** ###

*  intro: Rochester Institute of Technology
 *  arxiv: [https://arxiv.org/abs/1612.00390][https_arxiv.org_abs_1612.00390]

### **Abnormal Event Detection in Videos using Spatiotemporal Autoencoder** ###

*  arxiv: [https://arxiv.org/abs/1701.01546][https_arxiv.org_abs_1701.01546]
 *  github: [https://github.com/yshean/abnormal-spatiotemporal-ae][https_github.com_yshean_abnormal-spatiotemporal-ae]

### **Abnormal Event Detection in Videos using Generative Adversarial Nets** ###

*  intro: Best Paper / Student Paper Award Finalist, IEEE International Conference on Image Processing (ICIP), 2017
 *  arxiv: [https://arxiv.org/abs/1708.09644][https_arxiv.org_abs_1708.09644]

### **Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge** ###

*  intro: ICCV 2017
 *  arxiv: [https://arxiv.org/abs/1709.09121][https_arxiv.org_abs_1709.09121]

### **An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos** ###

*  intro: Uncanny Vision Solutions
 *  arxiv: [https://arxiv.org/abs/1801.03149][https_arxiv.org_abs_1801.03149]

### **STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection** ###

*  intro: ICASSP 2018
 *  arxiv: [https://arxiv.org/abs/1804.08381][https_arxiv.org_abs_1804.08381]

### **Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder** ###

*  [https://arxiv.org/abs/1805.11223][https_arxiv.org_abs_1805.11223]

# 视频预测 #

### **Deep multi-scale video prediction beyond mean square error** ###

*  intro: ICLR 2016
 *  arxiv: [http://arxiv.org/abs/1511.05440][http_arxiv.org_abs_1511.05440]
 *  github: [https://github.com/coupriec/VideoPredictionICLR2016][https_github.com_coupriec_VideoPredictionICLR2016]
 *  github(TensorFlow): [https://github.com/dyelax/Adversarial\_Video\_Generation][https_github.com_dyelax_Adversarial_Video_Generation]
 *  demo: [http://cs.nyu.edu/~mathieu/iclr2016.html][http_cs.nyu.edu_mathieu_iclr2016.html]

### **Unsupervised Learning for Physical Interaction through Video Prediction** ###

*  intro: NIPS 2016
 *  arxiv: [https://arxiv.org/abs/1605.07157][https_arxiv.org_abs_1605.07157]
 *  github: [https://github.com/tensorflow/models/tree/master/video\_prediction][https_github.com_tensorflow_models_tree_master_video_prediction]

### **Generating Videos with Scene Dynamics** ###

*  intro: NIPS 2016
 *  intro: The model learns to generate tiny videos using adversarial networks
 *  project page: [http://web.mit.edu/vondrick/tinyvideo/][http_web.mit.edu_vondrick_tinyvideo]
 *  paper: [http://web.mit.edu/vondrick/tinyvideo/paper.pdf][http_web.mit.edu_vondrick_tinyvideo_paper.pdf]
 *  github: [https://github.com/cvondrick/videogan][https_github.com_cvondrick_videogan]

# #

# 预训练 #

### **Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning** ###

*  project page: [https://coxlab.github.io/prednet/][https_coxlab.github.io_prednet]
 *  arxiv: [http://arxiv.org/abs/1605.08104][http_arxiv.org_abs_1605.08104]
 *  github: [https://github.com/coxlab/prednet][https_github.com_coxlab_prednet]
 *  github: [https://github.com/e-lab/torch-prednet][https_github.com_e-lab_torch-prednet]

### **Diversity encouraged learning of unsupervised LSTM ensemble for neural activity video prediction** ###

*  arxiv: [https://arxiv.org/abs/1611.04899][https_arxiv.org_abs_1611.04899]

### **Video Ladder Networks** ###

*  inro: NIPS 2016 workshop on ML for Spatiotemporal Forecasting
 *  arxiv: [https://arxiv.org/abs/1612.01756][https_arxiv.org_abs_1612.01756]

### **Unsupervised Learning of Long-Term Motion Dynamics for Videos** ###

*  intro: Stanford University
 *  arxiv: [https://arxiv.org/abs/1701.01821][https_arxiv.org_abs_1701.01821]

### **One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network** ###

*  intro: NCCV 2016
 *  arxiv: [https://arxiv.org/abs/1702.04125][https_arxiv.org_abs_1702.04125]

### **Fully Context-Aware Video Prediction** ###

*  intro: ETH Zurich & NNAISENSE
 *  keywords: unsupervised learning through video prediction, Parallel Multi-Dimensional LSTM
 *  project page: [https://sites.google.com/view/contextvp][https_sites.google.com_view_contextvp]
 *  arxiv: [https://arxiv.org/abs/1710.08518][https_arxiv.org_abs_1710.08518]

### **Novel Video Prediction for Large-scale Scene using Optical Flow** ###

*  intro: University of Victoria & Tongji University & Horizon Robotics
 *  arxiv: [https://arxiv.org/abs/1805.12243][https_arxiv.org_abs_1805.12243]

# 视频标注 #

### **Automatic Image and Video Tagging** ###

*  blog: [http://scottge.net/2015/06/30/automatic-image-and-video-tagging/][http_scottge.net_2015_06_30_automatic-image-and-video-tagging]

### **Tagging YouTube music videos with deep learning - Alexandre Passant** ###

*  keywords: Clarifai's deep learning API
 *  blog: [http://apassant.net/2015/07/03/tagging-youtube-music-clarifai-deep-learning/][http_apassant.net_2015_07_03_tagging-youtube-music-clarifai-deep-learning]

# 镜头边界检测 #

### **Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks** ###

[https://arxiv.org/abs/1705.03281][https_arxiv.org_abs_1705.03281]

### **Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks** ###

*  intro: obtains state-of-the-art results while running at an unprecedented speed of more than 120x real-time.
 *  arxiv: [https://arxiv.org/abs/1705.08214][https_arxiv.org_abs_1705.08214]

# 视频行为分割 #

### **TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation** ###

*  intro: University of Rochester
 *  arxiv: [https://arxiv.org/abs/1705.07818][https_arxiv.org_abs_1705.07818]

# 视频生成gif #

### **Video2GIF: Automatic Generation of Animated GIFs from Video (Robust Deep RankNet)** ###

*  intro: 3D CNN, ranking model, Huber loss, 100K GIFs/video sources dataset
 *  arxiv: [http://arxiv.org/abs/1605.04850][http_arxiv.org_abs_1605.04850]
 *  github(dataset): [https://github.com/gyglim/video2gif\_dataset][https_github.com_gyglim_video2gif_dataset]
 *  results: [http://video2gif.info/][http_video2gif.info]
 *  demo site: [http://people.ee.ethz.ch/~gyglim/work\_public/autogif/][http_people.ee.ethz.ch_gyglim_work_public_autogif]
 *  review: [http://motherboard.vice.com/read/these-fire-gifs-were-made-by-artificial-intelligence-yahoo][http_motherboard.vice.com_read_these-fire-gifs-were-made-by-artificial-intelligence-yahoo]

### **Creating Animated GIFs Automatically from Video** ###

[https://yahooresearch.tumblr.com/post/148009705216/creating-animated-gifs-automatically-from-video][https_yahooresearch.tumblr.com_post_148009705216_creating-animated-gifs-automatically-from-video]

[http_arxiv.org_abs_1609.00866]: http://arxiv.org/abs/1609.00866
[https_arxiv.org_abs_1612.00390]: https://arxiv.org/abs/1612.00390
[https_arxiv.org_abs_1701.01546]: https://arxiv.org/abs/1701.01546
[https_github.com_yshean_abnormal-spatiotemporal-ae]: https://github.com/yshean/abnormal-spatiotemporal-ae
[https_arxiv.org_abs_1708.09644]: https://arxiv.org/abs/1708.09644
[https_arxiv.org_abs_1709.09121]: https://arxiv.org/abs/1709.09121
[https_arxiv.org_abs_1801.03149]: https://arxiv.org/abs/1801.03149
[https_arxiv.org_abs_1804.08381]: https://arxiv.org/abs/1804.08381
[https_arxiv.org_abs_1805.11223]: https://arxiv.org/abs/1805.11223
[http_arxiv.org_abs_1511.05440]: http://arxiv.org/abs/1511.05440
[https_github.com_coupriec_VideoPredictionICLR2016]: https://github.com/coupriec/VideoPredictionICLR2016
[https_github.com_dyelax_Adversarial_Video_Generation]: https://github.com/dyelax/Adversarial_Video_Generation
[http_cs.nyu.edu_mathieu_iclr2016.html]: http://cs.nyu.edu/~mathieu/iclr2016.html
[https_arxiv.org_abs_1605.07157]: https://arxiv.org/abs/1605.07157
[https_github.com_tensorflow_models_tree_master_video_prediction]: https://github.com/tensorflow/models/tree/master/video_prediction
[http_web.mit.edu_vondrick_tinyvideo]: http://web.mit.edu/vondrick/tinyvideo/
[http_web.mit.edu_vondrick_tinyvideo_paper.pdf]: http://web.mit.edu/vondrick/tinyvideo/paper.pdf
[https_github.com_cvondrick_videogan]: https://github.com/cvondrick/videogan
[https_coxlab.github.io_prednet]: https://coxlab.github.io/prednet/
[http_arxiv.org_abs_1605.08104]: http://arxiv.org/abs/1605.08104
[https_github.com_coxlab_prednet]: https://github.com/coxlab/prednet
[https_github.com_e-lab_torch-prednet]: https://github.com/e-lab/torch-prednet
[https_arxiv.org_abs_1611.04899]: https://arxiv.org/abs/1611.04899
[https_arxiv.org_abs_1612.01756]: https://arxiv.org/abs/1612.01756
[https_arxiv.org_abs_1701.01821]: https://arxiv.org/abs/1701.01821
[https_arxiv.org_abs_1702.04125]: https://arxiv.org/abs/1702.04125
[https_sites.google.com_view_contextvp]: https://sites.google.com/view/contextvp
[https_arxiv.org_abs_1710.08518]: https://arxiv.org/abs/1710.08518
[https_arxiv.org_abs_1805.12243]: https://arxiv.org/abs/1805.12243
[http_scottge.net_2015_06_30_automatic-image-and-video-tagging]: http://scottge.net/2015/06/30/automatic-image-and-video-tagging/
[http_apassant.net_2015_07_03_tagging-youtube-music-clarifai-deep-learning]: http://apassant.net/2015/07/03/tagging-youtube-music-clarifai-deep-learning/
[https_arxiv.org_abs_1705.03281]: https://arxiv.org/abs/1705.03281
[https_arxiv.org_abs_1705.08214]: https://arxiv.org/abs/1705.08214
[https_arxiv.org_abs_1705.07818]: https://arxiv.org/abs/1705.07818
[http_arxiv.org_abs_1605.04850]: http://arxiv.org/abs/1605.04850
[https_github.com_gyglim_video2gif_dataset]: https://github.com/gyglim/video2gif_dataset
[http_video2gif.info]: http://video2gif.info/
[http_people.ee.ethz.ch_gyglim_work_public_autogif]: http://people.ee.ethz.ch/~gyglim/work_public/autogif/
[http_motherboard.vice.com_read_these-fire-gifs-were-made-by-artificial-intelligence-yahoo]: http://motherboard.vice.com/read/these-fire-gifs-were-made-by-artificial-intelligence-yahoo
[https_yahooresearch.tumblr.com_post_148009705216_creating-animated-gifs-automatically-from-video]: https://yahooresearch.tumblr.com/post/148009705216/creating-animated-gifs-automatically-from-video