This repository is originating from our survey paper "Unifying Video Self-Supervised Learning across Families of Tasks: A Survey" and authors (Ishan Dave*, Malitha Gunawardhana*, Limalka Sadith, Honglu Zhou, Liel David, Daniel Harari, Mubarak Shah, Muhammad Haris Khan) will continue to update this over time.
Abstract: Video self-supervised learning (VideoSSL) offers significant potential for reducing annotation costs and enhancing a wide range of downstream tasks in video understanding. The ultimate goal of VideoSSL is to achieve human-level video intelligence across a spectrum of tasks, from low-level tasks such as pixel temporal correspondence to high-level complex spatio-temporal tasks like action recognition. However, most existing VideoSSL methods focus on isolated aspects of this spectrum and fail to integrate different levels of task complexity. Our study presents the first comprehensive survey that connects all families of VideoSSL methods. We provide a detailed review of the full spectrum of VideoSSL, from low to high levels, by conceptually linking their self-supervised learning objectives and including a comprehensive categorization. Our extensive evaluation highlights the strengths and limitations of each SSL objective across various downstream task families. We also detail the challenges in current VideoSSL research such as data curation, interpretability, deployment, and privacy concerns, an area that previous surveys have not thoroughly explored. In addressing these challenges, we recognize the strengths of existing methods in addressing these challenges and outline future directions for research.
Overview of the three major families of video self-supervised learning methods. Dave and Gunawardhana et al. (2024)
This repository contains a collection of state-of-the-art self-supervised learning in video approaches for various downstream tasks, such as action recognition, video retrieval, etc. With the exponential growth of video data, there is an increasing need for automatic video analysis methods that can learn from large amounts of unlabeled data. Self-supervised learning provides an effective solution to this problem by allowing models to learn from the data itself without explicit supervision.
This research was supported by the joint grant P007 from Mohamed Bin Zayed University of Artificial Intelligence and the Weizmann Institute of Science. The authors would like to express their sincere gratitude for this generous support, which made the study possible.
If you find our work useful. Please consider giving a star ⭐ and a citation.
@article{dave2024unifying,
title={Unifying Video Self-Supervised Learning across Families of Tasks: A Survey},
author={Dave, Ishan and Gunawardhana, Malitha and Sadith, Limalka and Zhou, Honglu and David, Liel and Harari, Daniel and Shah, Mubarak and Khan, Muhammad Haris},
year={2024},
publisher={Preprints}
}
In this repository, we have gathered some of the most promising self-supervised learning approaches for video analysis and organized them based on their publication year. Whether you are new to self-supervised learning in videos or an experienced researcher in the field, we hope that this repository will serve as a valuable resource for exploring the latest advances in this exciting area of research.
Let's collaborate and enrich this list together! Reach out to me or submit a pull request. Your contributions are highly appreciated.
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Unifying Video Self-Supervised Learning across Families of Tasks: A Survey (2024)
Preprint
Ishan Dave*, Malitha Gunawardhana*, Limalka Sadith, Honglu Zhou, Liel David, Daniel Harari, Mubarak Shah, Muhammad Hairs Khan
[Paper] -
Self-Supervised Learning for Videos: A Survey (2022)
ACM Computing Surveys
Madeline C. Schiappa, Yogesh S. Rawat, And Mubarak Shah
[Paper]
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How Effective are Self-Supervised Models for Contact Identification in Videos (2024)
arXiv preprint
Malitha Gunawardhana, Limalka Sadith, Liel David, Daniel Harari, Muhammad Haris Khan
[Paper] [Code] -
Benchmarking self-supervised video representation learning (2023)
arXiv preprint arXiv:2306.06010
Akash Kumar, Ashlesha Kumar, Vibhav Vineet, Yogesh Singh Rawat
[Paper] [Page] -
A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition (2023)
arXiv preprint arXiv:2303.13505
Deng, A., Yang, T., & Chen, C.
[Paper] -
How Severe Is Benchmark-Sensitivity in Video Self-supervised Learning? (2022, October)
In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022
Fida Mohammad Thoker, Hazel Doughty, Piyush Bagad, Cees Snoek
[Paper] [Github] [Page]
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ST2ST: Self-Supervised Test-time Adaptation for Video Action Recognition* (2024)
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops4
Masud An-Nur Islam Fahim, Mohammed Innat, Jani Boutellier;
[Paper] -
Self-supervised learning of video representations from a child's perspective* (2024)
CogSci 2024
A. Emin Orhan, Wentao Wang, Alex N. Wang, Mengye Ren, Brenden M. Lake;
[Paper][Code] -
Learning Video Representations without Natural Videos* (2024)
Arxiv
Xueyang Yu, Xinlei Chen, Yossi Gandelsman;
[Paper] -
ViC-MAE: Self-supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders (2024)
European Conference on Computer Vision (ECCV), 2024
Jefferson Hernandez, Ruben Villegas, Vicente Ordonez;
[Paper][Code] -
Learning to Predict Activity Progress by Self-Supervised Video Alignment (2024)
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Gerard Donahue, Ehsan Elhamifar;
[Paper][Code] -
Repeat and learn: Self-supervised visual representations learning by Repeated Scene Localization (2024)
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Yuanhang Zhang, Shuang Yang, Shiguang Shan, Xilin Chen;
[Paper][Code] -
ES3: Evolving Self-Supervised Learning of Robust Audio-Visual Speech Representations (2024)
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Yuanhang Zhang, Shuang Yang, Shiguang Shan, Xilin Chen;
[Paper] -
Self-supervised Learning of Semantic Correspondence Using Web Videos (2024)
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024
Donghyeon Kwon, Minsu Cho, Suha Kwak;
[Paper] -
Video Compression and Action Recognition in Self-supervised Learning (2024)
2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)
Zongbo Hao; Conghui Hao; Kecheng He
[Paper] -
CycleCL: Self-supervised Learning for Periodic Videos (2024)
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024
Matteo Destro, Michael Gygl
[Paper] -
Self-Supervised Learning via Multi-Transformation Classification for Action Recognition (2024)
2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)
Duc-Quang Vu; Ngan Le; Jia-Ching Wang
[Paper] -
Self-supervised object-centric learning for videos (2024)
Advances in Neural Information Processing Systems (Neurips) 2024
Görkay Aydemir, Weidi Xie, Fatma Guney
[Paper] -
CycleCL: Self-supervised Learning for Periodic Videos (2024)
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024
Matteo Destro, Michael Gygli
[Paper] -
Motion-guided spatiotemporal multitask feature discrimination for self-supervised video representation learning (2024)
Pattern Recognition (Elsevier)
Shuai Bi, Zhengping Hu, Hehao Zhang, Jirui Di, Zhe Sun
[Paper] -
What When and Where? Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions (2024)
Computer Vision and Pattern Recognition (CVPR), 2024
Brian Chen, Nina Shvetsova, Andrew Rouditchenko, Daniel Kondermann, Samuel Thomas, Shih-Fu Chang, Rogerio Feris, James Glass, Hilde Kuehne
[Paper] -
BIMM: Brain Inspired Masked Modeling for Video Representation Learning (2024)
arxiv
Zhifan Wan, Jie Zhang, Changzhen Li, Shiguang Shan
[Paper] -
Clustering-based multi-featured self-supervised learning for human activities and video retrieval (2024)
Applied Intelligence - Springer
Muhammad Hafeez Javed, Zeng Yu, Taha M. Rajeh, Fahad Rafique & Tianrui Li
[Paper] -
Positive and negative sampling strategies for self-supervised learning on audio-video data (2024)
arxiv
Shanshan Wang, Soumya Tripathy, Toni Heittola, Annamaria Mesaros
[Paper] -
Self-supervised learning of video representations from a child's perspective (2024)
arxiv
Emin Orhan, Wentao Wang, Alex N. Wang, Mengye Ren, Brenden M. Lake
[Paper] -
Collaboratively Self-supervised Video Representation Learning for Action Recognition (2024)
arxiv
Jie Zhang, Zhifan Wan, Lanqing Hu, Stephen Lin, Shuzhe Wu, Shiguang Shan
[Paper] -
Language-based Action Concept Spaces Improve Video Self-Supervised Learning (2024)
Advances in Neural Information Processing Systems 36 (2024)
Kanchana Ranasinghe, Michael S Ryoo
[Paper] -
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts (2024)
Advances in Neural Information Processing Systems 36 (2024)
Pritam Sarkar, Ahmad Beirami, Ali Etemad
[Paper] [Project Page] -
Self-supervised video pretraining yields robust and more human-aligned visual representation (2024)
Advances in Neural Information Processing Systems 36 (2024)
Nikhil Parthasarathy, S. M. Ali Eslami, João Carreira, Olivier J. Hénaff.
[Paper] -
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision (2024)
AAAI Conference on Artificial Intelligence, Main Technical Track (AAAI) , 2024
Ishan Rajendrakumar Dave, Simon Jenni, Mubarak Shah.
[Paper] [Project Page] -
GLOCAL: A self-supervised learning framework for global and local motion estimation (2024)
Pattern Recognition Letters
Yihao Zheng , Kunming Luo , Shuaicheng Liu , Zun Li , Ye Xiang , Lifang Wu , Bing Zeng , Chang Wen Chen
[Paper]
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OmniMAE: Single Model Masked Pretraining on Images and Videos (2023)
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Rohit Girdhar, Alaaeldin El-Nouby, Mannat Singh,Kalyan Vasudev Alwala, Armand Joulin , Ishan Misra
[Paper] [Github] -
TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action Recognition (2023)
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Ishan Rajendrakumar Dave, Mamshad Nayeem Rizve, Chen Chen, Mubarak Shah
[Paper] [Github] [Project Page] -
Self-supervised Video Representation Learning via Capturing Semantic Changes Indicated by Saccades (2023)
IEEE Transactions on Circuits and Systems for Video Technology
Qiuxia Lai, Ailing Zeng, Ye Wang, Lihong Cao, Yu Li, Qiang Xu, IEEE
[Paper] -
Attentive spatial-temporal contrastive learning for self-supervised video representation (2023)
Image and Vision Computing Journal
Xingming Yang, Sixuan Xiong, Kewei Wu, Dongfeng Shan, Zhao Xie
[Paper] -
Attentive spatial-temporal contrastive learning for self-supervised video representation (2023)
Image and Vision Computing Journal
Xingming Yang, Sixuan Xiong, Kewei Wu, Dongfeng Shan, Zhao Xie
[Paper] -
Cross-modal Manifold Cutmix for Self-supervised Video Representation Learning (2023)
18th International Conference on Machine Vision and Applications (MVA) 2023
Srijan Das; Michael Ryoo
[Paper] -
CHAIN: Exploring Global-Local Spatio-Temporal Information for Improved Self-Supervised Video Hashing (2023)
Proceedings of the 31st ACM International Conference on Multimedia
Rukai Wei, Yu Liu, Jingkuan Song, Heng Cui, Yanzhao Xie, Ke Zhou
[Paper] -
Data-Efficient Masked Video Modeling for Self-supervised Action Recognition (2023)
Proceedings of the 31st ACM International Conference on Multimedia
Qiankun Li, Xiaolong Huang, Zhifan Wan, Lanqing Hu, Shuzhe Wu, Jie Zhang, Shiguang Shan, Zengfu Wang(
[Paper] -
MAR: Masked Autoencoders for Efficient Action Recognition (2023)
IEEE Transactions on Multimedia
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Xiang Wang, Yuehuan Wang, Yiliang Lv, Changxin Gao, Nong Sang
[Paper] [Github] -
Temporal Transformer Networks with Self-Supervision for Action Recognition (2023)
IEEE Internet of Things Journal
Yongkang Zhang, Jun Li, Guoming Wu, Han Zhang, Zhiping Shi, Member, IEEE, Zhaoxun Liu, Zizhang Wu
[Paper] -
CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition (2023)
arXiv preprint arXiv:2301.06018
Cheng-Ze Lu, Xiaojie Jin, Zhicheng Huang, Qibin Hou, Ming-Ming Cheng, Jiashi Feng
[Paper] -
Learning Representational Invariances for Data-Efficient Action Recognition (2023)
Computer Vision and Image Understanding, 227, 103597
Yuliang Zou, Jinwoo Choi, Qitong Wang, Jia-Bin Huang
[Paper] [Github] -
SOR-TC: Self-attentive octave ResNet with temporal consistency for compressed video action recognition (2023)
Neurocomputing, 533, 191-205
Junsan Zhang, Xiaomin Wang, Yao Wan, Leiquan Wang, Jian Wang, Philip S. Yu
[Paper] -
VicTR: Video-conditioned Text Representations for Activity Recognition (2023)
arXiv preprint arXiv:2304.02560
Kumara Kahatapitiya, Anurag Arnab, Arsha Nagrani, Michael S. Ryoo
[Paper] -
Masked Motion Encoding for Self-Supervised Video Representation Learning (2023)
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Xinyu Sun, Peihao Chen, Liangwei Chen, Thomas H. Li, Mingkui Tan, Chuang Gan
[Paper] [Github] -
Spatiotemporal consistency enhancement self-supervised representation learning for action recognition (2023)
Signal, Image and Video Processing
Shuai Bi, Zhengping Hu, Mengyao Zhao, Shufang Li & Zhe Sun
[Paper] -
Self-Supervised Video-Based Action Recognition With Disturbances (2023)
IEEE Transactions on Image Processing.
Wei Lin, Xinghao Ding, Yue Huang, Huanqiang Zeng
[Paper] -
Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning (2023)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6312-6322)
Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Lu Yuan, Yu-Gang Jiang
[Paper] [Github] -
Enhancing motion visual cues for self-supervised video representation learning (2023)
In Engineering Applications of Artificial Intelligence, Volume 123, Pages 106203
Mu Nie, Zhibin Quan, Weiping Ding, and Wankou Yang
[Paper] -
Continuous frame motion sensitive self-supervised collaborative network for video representation learning (2023)
In Advanced Engineering Informatics, Volume 56, Pages 101941
Shuai Bi, Zhengping Hu, Mengyao Zhao, Hehao Zhang, Jirui Di, and Zhe Sun
[Paper] -
Self-supervised pretext task collaborative multi-view contrastive learning for video action recognition (2023)
In Signal, Image and Video Processing, Pages 1--8
Shuai Bi, Zhengping Hu, Mengyao Zhao, Hehao Zhang, Jirui Di, and Zhe Sun
[Paper] -
Self-Supervised Learning from Untrimmed Videos via Hierarchical Consistency (2023)
In IEEE Transactions on Pattern Analysis and Machine Intelligence
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Changxin Gao, Rong Jin, and Nong Sang
[Paper] -
Self-Supervised Video Representation Learning by Video Incoherence Detection (2023)
In IEEE Transactions on Cybernetics
Haozhi Cao, Yuecong Xu, Kezhi Mao, Lihua Xie, Jianxiong Yin, Simon See, Qianwen Xu, and Jianfei Yang
[Paper] -
Audio-Visual Contrastive Learning with Temporal Self-Supervision (2023)
Preprint on arXiv
Simon Jenni, Alexander Black, and John Collomosse
[Paper] -
Video Test-Time Adaptation for Action Recognition (2023)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Wei Lin, Muhammad Jehanzeb Mirza, Mateusz Kozinski, Horst Possegger, Hilde Kuehne, and Horst Bischof
[Paper] [GitHub]
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Self-Supervised Video Representation Learning via Latent Time Navigation (2023)
Preprint on arXiv
Di Yang, Yaohui Wang, Quan Kong, Antitza Dantcheva, Lorenzo Garattoni, Gianpiero Francesca, and Francois Bremond
[Paper] -
Temporal Contrastive Learning with Curriculum (2023)
In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Shuvendu Roy and Ali Etemad
[Paper] -
Nearest-Neighbor Inter-Intra Contrastive Learning from Unlabeled Videos (2023)
Preprint on arXiv
David Fan, Deyu Yang, Xinyu Li, Vimal Bhat, and Rohith MV
[Paper] -
Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization (2023)
Preprint on arXiv
Fida Mohammad Thoker, Hazel Doughty, and Cees Snoek
[Paper] -
Multi-scale Compositional Constraints for Representation Learning on Videos (2023)
In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Georgios Paraskevopoulos, Chandrashekhar Lavania, Lovish Chum, and Shiva Sundaram
[Paper] -
Flavr: Flow-agnostic Video Representations for Fast Frame Interpolation (2023)
In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
Tarun Kalluri, Deepak Pathak, Manmohan Chandraker, and Du Tran
[Paper] -
HomE: Homography-Equivariant Video Representation Learning (2023)
Preprint on arXiv
Anirudh Sriram, Adrien Gaidon, Jiajun Wu, Juan Carlos Niebles, Li Fei-Fei, and Ehsan Adeli
[Paper] [GitHub] -
ViewCLR: Learning Self-supervised Video Representation for Unseen Viewpoints (2023)
In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
Srijan Das and Michael S Ryoo
[Paper] -
Videomae v2: Scaling Video Masked Autoencoders with Dual Masking (2023)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Limin Wang, Bingkun Huang, Zhiyu Zhao, Zhan Tong, Yinan He, Yi Wang, Yali Wang, and Yu Qiao
[Paper] -
Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity (2023)
Proceedings of the AAAI Conference on Artificial Intelligence
Pritam Sarkar, Ali Etemad
[Paper] [Github] -
Spatiotemporally Discriminative Video-Language Pre-Training with Text Grounding (2023)
Spatiotemporally Discriminative Video-Language Pre-Training with Text Grounding
Xiong, Y., Zhao, L., Gong, B., Yang, M. H., Schroff, F., Liu, T., ... & Yuan, L.
[Paper] -
Previts: contrastive pretraining with video tracking supervision (2023)
In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 1560-1570)
Chen, B., Selvaraju, R. R., Chang, S. F., Niebles, J. C., & Naik, N.
[Paper] -
Modeling Video As Stochastic Processes for Fine-Grained Video Representation Learning (2023)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2225-2234)
Zhang, H., Liu, D., Zheng, Q., & Su, B.
[Paper] -
Learning Fine-Grained Features for Pixel-wise Video Correspondences (2023)
arXiv preprint arXiv:2308.03040
Li, R., Zhou, S., & Liu, D.
[Paper] -
Cali-NCE: Boosting Cross-Modal Video Representation Learning With Calibrated Alignment (2023)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6316-6326)
Zhao, N., Jiao, J., Xie, W., & Lin, D.
[Paper]
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SPAct: Self-supervised Privacy Preservation for Action Recognition (2022)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20164-20173)
Ishan Rajendrakumar Dave, Chen Chen, Mubarak Shah
[Paper] [Github] -
Suppressing Static Visual Cues via Normalizing Flows for Self-Supervised Video Representation Learning (2022, June)
In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 3, pp. 3300-3308)
Manlin Zhang, Jinpeng Wang, Andy J. Ma
[Paper] [Github] -
Self-supervised Video Representation Learning with Motion-Aware Masked Autoencoders (2022)
arXiv preprint arXiv:2210.04154
Haosen Yang, Deng Huang, Bin Wen, Jiannan Wu, Hongxun Yao, Yi Jiang, Xiatian Zhu, Zehuan Yuan
[Paper] [Github] -
Self-supervised Video Transformer (2022)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2874-2884)
Kanchana Ranasinghe, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, Michael S. Ryoo
[Paper] [Github] -
Exploring Relations in Untrimmed Videos for Self-Supervised Learning (2022)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM),18(1s), 1-21
Dezhao Luo, Bo Fang, Yu Zhou, Yucan Zhou, Dayan Wu, Weiping Wang
[Paper] -
MaMiCo: Macro-to-Micro Semantic Correspondence for Self-supervised Video Representation Learning (2022, October)
In Proceedings of the 30th ACM International Conference on Multimedia (pp. 1348-1357)
Bo Fang, Wenhao Wu, Chang Liu, Yu Zhou, Dongliang He, Weiping Wang
[Paper] -
TCGL: Temporal Contrastive Graph for Self-Supervised Video Representation Learning (2022)
IEEE Transactions on Image Processing, 31, 1978-1993
Yang Liu , Keze Wang , Lingbo Liu , Haoyuan Lan, and Liang Lin
[Paper] [Github] -
Cross-Architecture Self-supervised Video Representation Learning (2022)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 19270-19279)
Sheng Guo, Zihua Xiong, Yujie Zhong, Limin Wang, Xiaobo Guo, Bing Han, Weilin Huang
[Paper] -
Contrastive spatio-temporal pretext learning for self-supervised video representation (2022, June)
In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 3, pp. 3380-3389)
Yujia Zhang, Lai-Man Po, Xuyuan Xu, Mengyang Liu, Yexin Wang, Weifeng Ou, Yuzhi Zhao, Wing-Yin Yu
[Paper] [Github] -
Transrank: Self-supervised video representation learning via ranking-based transformation recognition (2022)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3000-3010)
Haodong Duan, Nanxuan Zhao, Kai Chen, Dahua Lin
[Paper] [Github] -
Learning from untrimmed videos: Self-supervised video representation learning with hierarchical consistency (2022)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13821-13831)
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin,Nong Sang
[Paper] [Github] -
Motion-aware contrastive video representation learning via foreground-background merging (2022)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9716-9726)
Shuangrui Ding, Maomao Li, Tianyu Yang, Rui Qian, Haohang Xu, Qingyi Chen, Jue Wang, Hongkai Xiong
[Paper] [Github] -
Self-Supervised Video Representation Learning with Motion-Contrastive Perception (2022, July)
In 2022 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE
Jinyu Liu, Ying Cheng, Yuejie Zhang, Rui-Wei Zhao, Rui Feng
[Paper] -
Self-supervised video representation learning using improved instance-wise contrastive learning and deep clustering (2022)
IEEE Transactions on Circuits and Systems for Video Technology, 32(10), 6741-6752
Yisheng Zhu, Hui Shuai, Guangcan Liu, Senior Member, Qingshan Liu
[Paper] -
TCLR: Temporal contrastive learning for video representation (2022)
Computer Vision and Image Understanding, 219, 103406
Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve, Mubarak Shah
[Paper] [Github] -
Self-supervised motion perception for spatiotemporal representation learning (2022)
IEEE Transactions on Neural Networks and Learning Systems
Chang Liu, Yuan Yao, Dezhao Luo, Yu Zhou, Qixiang Ye
[Paper] [Github] -
Self-supervised spatiotemporal representation learning by exploiting video continuity (2022, June)
In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 2, pp. 1564-1573)
Hanwen Liang, Niamul Quader, Zhixiang Chi, Lizhe Chen, Peng Dai, Juwei Lu, Yang Wang
[Paper] -
Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised Learning (2022)
arXiv preprint arXiv:2212.11187
Julien Denize, Jaonary Rabarisoa, Astrid Orcesi, Romain H´erault
[Paper] [Github] -
Probabilistic representations for video contrastive learning (2022)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14711-14721)
Jungin Park, Jiyoung Lee, Ig-Jae Kim, Kwanghoon Sohn
[Paper] -
Contextualized spatio-temporal contrastive learning with self-supervision (2022)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13977-13986)
Liangzhe Yuan, Rui Qian, Yin Cui, Boqing Gong,Florian Schroff,Ming-Hsuan Yang, Hartwig Adam, Ting Liu
[Paper] [Github] -
Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training (2022)
In Advances in Neural Information Processing Systems, 2022
Zhan Tong, Yibing Song, Jue Wang, Limin Wang
[Paper] [Github] -
Efficient Video Representation Learning via Masked Video Modeling with Motion-centric Token Selection (2022)
arXiv preprint arXiv:2211.10636
Sunil Hwang, Jaehong Yoon, Youngwan Lee, Sung Ju Hwan
[Paper] [Github] -
Self-supervised video representation learning with cross-stream prototypical contrasting (2022)
In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 108-118)
Martine Toering, Ioannis Gatopoulos, Maarten Stol, Vincent Tao Hu
[Paper] [Github] -
SLIC: Self-supervised learning with iterative clustering for human action videos (2022)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16091-16101)
Salar Hosseini Khorasgani, Yuxuan Chen, Florian Shkurti
[Paper] -
GOCA: guided online cluster assignment for self-supervised video representation Learning (2022, October)
In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022
Huseyin Coskun, Alireza Zareian, Joshua L. Moore, Federico Tombari, Chen Wang
[Paper] [Github] -
TCVM: Temporal Contrasting Video Montage Framework for Self-supervised Video Representation Learning (2022)
In Proceedings of the Asian Conference on Computer Vision (pp. 1539-1555)
Fengrui Tian, Jiawei Fan, Xie Yu, Shaoyi Du, Meina Song, Yu Zhao
[Paper] -
Static and Dynamic Concepts for Self-supervised Video Representation Learning (2022, November)
In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022
Rui Qian, Shuangrui Ding, Xian Liu, Dahua Lin
[Paper] -
Audio-Visual Contrastive Learning for Self-Supervised Action Recognition (2022)
arXiv preprint arXiv:2204.13386
Yang Liu, Ying Tan, Haoyuan Lan
[Paper] -
SOS! Self-supervised Learning over Sets of Handled Objects in Egocentric Action Recognition (2022, November)
In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022
Victor Escorcia, Ricardo Guerrero, Xiatian Zhu, Brais Martinez
[Paper] -
Self-Supervised Video Representation Learning with Cascade Positive Retrieval (2022)
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4070-4079)
Cheng-En Wu, Farley Lai, Yu Hen Hu, Asim Kadav
[Paper] [Github] -
Self-Supervised Learning of Audio Representations From Audio-Visual Data Using Spatial Alignment (2022)
IEEE Journal of Selected Topics in Signal Processing,16(6), 1467-1479
Shanshan Wang, Archontis Politis, Annamaria Mesaros
[Paper] -
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