Vid2speech: Speech Reconstruction from Silent Video

• intro: ICASSP 2017
• project page: http://www.vision.huji.ac.il/vid2speech/
• arxiv: https://arxiv.org/abs/1701.00495
• github(official): https://github.com/arielephrat/vid2speech

Video summarization produces a short summary of a full-length video and ideally encapsulates its most informative parts,深度视频视频 alleviates the problem of video browsing, editing and indexing.

Video Summarization with Long Short-term Memory

• arxiv: http://arxiv.org/abs/1605.08110

DeepVideo: Video Summarization using Temporal Sequence Modelling

• intro: CS231n student project report
• paper: http://cs231n.stanford.edu/reports2016/216_Report.pdf

Semantic Video Trailers

• arxiv: http://arxiv.org/abs/1609.01819

Video Summarization using Deep Semantic Features

• inro: ACCV 2016
• arxiv: http://arxiv.org/abs/1609.08758

CNN-Based Prediction of Frame-Level Shot Importance for Video Summarization

• intro: International Conference on new Trends in Computer Sciences (ICTCS), Amman-Jordan, 2017
• arxiv: https://arxiv.org/abs/1708.07023

Video Summarization with Attention-Based Encoder-Decoder Networks

https://arxiv.org/abs/1708.09545

Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward

• intro: AAAI 2018. Chinese Academy of Sciences & Queen Mary University of London
• project page: https://kaiyangzhou.github.io/project_vsumm_reinforce/index.html
• arxiv: https://arxiv.org/abs/1801.00054
• github: https://github.com//KaiyangZhou/vsumm-reinforce

Viewpoint-aware Video Summarization

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1804.02843

DTR-GAN: Dilated Temporal Relational Adversarial Network for Video Summarization

https://arxiv.org/abs/1804.11228

Learning Video Summarization Using Unpaired Data

https://arxiv.org/abs/1805.12174

Video Summarization Using Fully Convolutional Sequence Networks

https://arxiv.org/abs/1805.10538

Video Summarisation by Classification with Deep Reinforcement Learning

• intro: BMVC 2018
• arxiv: https://arxiv.org/abs/1807.03089

Query-Conditioned Three-Player Adversarial Network for Video Summarization

• intro: BMVC 2018
• arxiv: https://arxiv.org/abs/1807.06677

Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders

• intro: ICCV 2015
• intro: rely on an assumption that highlights of an event category are more frequently captured in short videos than non-highlights
• arxiv: http://arxiv.org/abs/1510.01442

Highlight Detection with Pairwise Deep Ranking for First-Person Video Summarization

• keywords: wearable device
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yao_Highlight_Detection_With_CVPR_2016_paper.pdf
• paper: http://research.microsoft.com/apps/pubs/default.aspx?id=264919

Using Deep Learning to Find Basketball Highlights

• blog: http://public.hudl.com/bits/archives/2015/06/05/highlights/?utm_source=tuicool&utm_medium=referral

Real-Time Video Highlights for Yahoo Esports

• arxiv: https://arxiv.org/abs/1611.08780

A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1801.10312

PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation

• intro: Nanyang Technological University & Google Research, Zurich
• keywords: personalized highlight detection (PHD)
• arxiv: https://arxiv.org/abs/1804.06604

Scale Up Video Understandingwith Deep Learning

• intro: 2016, Tsinghua University
• slides: iiis.tsinghua.edu.cn/~jianli/courses/ATCS2016spring/talk_chuang.pptx

Slicing Convolutional Neural Network for Crowd Video Understanding

• intro: CVPR 2016
• intro: It aims at learning generic spatio-temporal features from crowd videos, especially for long-term temporal learning
• project page: http://www.ee.cuhk.edu.hk/~jshao/SCNN.html
• paper: http://www.ee.cuhk.edu.hk/~jshao/papers_jshao/jshao_cvpr16_scnn.pdf
• github: https://github.com/amandajshao/Slicing-CNN

Rethinking Spatiotemporal Feature Learning For Video Understanding

https://arxiv.org/abs/1712.04851

Hierarchical Video Understanding

https://arxiv.org/abs/1809.03316

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