The DOI matches its Crossref record; 98 of the 104 references it cites were found in a registry or an index.
A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning
Open accessAbstract
We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart. Code will be made available at https://github.com/facebookresearch/SlowFast.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Self-citationVery Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Self-citationGoing deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Self-citationHe, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
Self-citationGirshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Self-citationBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
(2010) · International Conference on Machine Learning · Cited by 13,995
Flagged first, then by citations
6 of the 104 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- girshick, 2015, Fast r-cnn
- girshick, 2018, Detectron
- 10.1109/ICCV.2019.00996 · DOI
- fan, 2020, Pyslowfast
Cited by
Sorted by citations
Items 101–200 of 224
Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization
Thoker, F. M., Doughty, H., & Snoek, C. G. M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01270 · Cited by 12
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision
Dave, I. R., Jenni, S., & Shah, M. A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27913 · Cited by 11
Bi-calibration Networks for Weakly-Supervised Video Representation Learning
Long, F., Yao, T., Qiu, Z., Tian, X., Luo, J., & Mei, T. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01779-w · Cited by 10
Similarity contrastive estimation for image and video soft contrastive self-supervised learning
Denize, J., Rabarisoa, J., Orcesi, A., & Hérault, R. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01444-9 · Cited by 11
Semantics Meets Temporal Correspondence: Self-supervised Object-centric Learning in Videos
Qian, R., Ding, S., Liu, X., & Lin, D.-H. (2023) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51070.2023.01529 · Cited by 27
SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning
Thoker, F. M., Jiang, L., Zhao, C., & Ghanem, B. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00790 · Cited by 8
Kimata, J., Nitta, T., & Tamaki, T. (2022) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3551626.3564941 · Cited by 8
A Survey of Recent Advances and Challenges in Deep Audio-Visual Correlation Learning
Vilaça, L., Yu, Y., & Viana, P. (2025) · ACM Computing Surveys · doi:10.1145/3696445 · Cited by 17
Bi, S., Hu, Z., Zhao, M., Zhang, H., Di, J., & Sun, Z. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101941 · Cited by 7
Convolutional Attention Based Mechanism for Facial Microexpression Recognition
Talib, H. K. B., Xu, K., Cao, Y., Xu, Y., Xu, Z., Zaman, M., & Akhunzada, A. (2025) · IEEE Access · doi:10.1109/access.2024.3525151 · Cited by 7
Gramacki, P., Leśniara, K., Raczycki, K., Woźniak, S., Przymus, M., & Szymański, P. (2023) · ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI) · doi:10.1145/3615886.3627740 · Cited by 7
Bi, S., Hu, Z., Zhang, H., Di, J., & Sun, Z. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110713 · Cited by 7
Liu, Y., Xu, Q., Wen, P., Dai, S., & Huang, Q. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02238 · Cited by 8
Learning from Weakly-Labeled Web Videos via Exploring Sub-concepts
Li, K., Zhang, Z., Wu, G., Xiong, X., Lee, C., Lu, Z., Fu, Y., & Pfister, T. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20022 · Cited by 8
A Unified Framework for Layout Pattern Analysis with Deep Causal Estimation
Chen, R., Hu, S., Chen, Z., Zhu, S., Yu, B., Li, P., Cheng, C., Huang, Y., & Hao, J. (2021) · doi:10.1109/iccad51958.2021.9643458 · Cited by 7
Self-Supervised Video Representation Learning by Video Incoherence Detection
Cao, H., Xu, Y., Mao, K., Xie, L., Yin, J., See, S. C. W., Xu, Q., & Yang, J. (2023) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2023.3265393 · Cited by 9
Context-Aware Sequence Alignment using 4D Skeletal Augmentation
Kwon, T., Tekin, B., Tang, S., & Pollefeys, M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00800 · Cited by 11
Broomé, S., Pokropek, E., Li, B., & Kjellström, H. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00418 · Cited by 8
Self-supervised Video-centralised Transformer for Video Face Clustering
Wang, Y., Dong, M., Shen, J., Luo, Y., Lin, Y., Ma, P., Petridis, S., & Pantić, M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3243812 · Cited by 8
Solution of wide and micro background bias in contrastive action representation learning
Liu, S., Luo, Z., Li, Y., Wang, Y., Fu, W., & Ding, W. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108244 · Cited by 7
MaMiCo: Macro-to-Micro Semantic Correspondence for Self-supervised Video Representation Learning
Fang, B., Wu, W., Liu, C., Zhou, Y., He, D., & Wang, W. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547888 · Cited by 8
Zhang, L., She, Q., Shen, Z., & Wang, C. (2021) · arXiv (Cornell University) · Cited by 7
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learning
Zhu, M., Lin, X., Dang, R., Liu, C., & Chen, Q. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611932 · Cited by 8
DPPMask: Masked Image Modeling with Determinantal Point Processes
Xu, J., Lin, Z., Zhou, D., Yang, Y., Liao, X., Wang, Q., Wu, B., Chen, G., & Heng, P. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00226 · Cited by 7
Multi-modal Unsupervised Pre-training for Surgical Operating Room Workflow Analysis
Jamal, M. A., & Mohareri, O. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16449-1_43 · Cited by 6
Action identification with fusion of BERT and 3DCNN for smart home systems
Lê, T. H., Le, T. M., & Nguyen, T. A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100811 · Cited by 7
Frequency Selective Augmentation for Video Representation Learning
Kim, J., Kim, T., Shim, M., Han, D., Wee, D., & Kim, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25194 · Cited by 8
Collaboratively Self-Supervised Video Representation Learning for Action Recognition
Zhang, J., Wan, Z., Hu, L., Lin, S., Wu, S., & Shan, S. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3531772 · Cited by 7
Vision-Language Adaptive Clustering and Meta-Adaptation for Unsupervised Few-Shot Action Recognition
Chen, J., Peng, J., Lu, Y., Lai, J., & Ma, A. J. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3558785 · Cited by 5
Self-Supervised Learning from Untrimmed Videos via Hierarchical Consistency
Qing, Z., Zhang, S., Huang, Z., Xu, Y., Wang, X., Gao, C., Jin, R., & Sang, N. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3273415 · Cited by 5
Freire-Obregón, D., Lorenzo-Navarro, J., Santana, O. J., Hernández-Sosa, D., & Castrillón-Santana, M. (2023) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb57857.2023.10448781 · Cited by 5
Spatiotemporal Representation Learning for Short and Long Medical Image Time Series
Shen, C., Menten, M. J., Bogunović, H., Schmidt-Erfurth, U. M., Scholl, H. P. N., Sivaprasad, S., Lotery, A. J., Rueckert, D., Hager, P., & Holland, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72120-5_61 · Cited by 5
The Journey of Action Recognition
Xi, D., & Wang, L. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3717746 · Cited by 6
GOCA: Guided Online Cluster Assignment for Self-supervised Video Representation Learning
Coskun, H., Zareian, A., Moore, J. L., Tombari, F., & Wang, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_1 · Cited by 5
Yang, X., Mirmehdi, M., & Burghardt, T. (2020) · Bristol Research (University of Bristol) · doi:10.48550/arxiv.2010.07217 · Cited by 5
Text-Guided Video Masked Autoencoder
Fan, D., Wang, J., Liao, S., Zhang, Z., Bhat, V., & Li, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72652-1_17 · Cited by 4
A Unified Framework for Layout Pattern Analysis With Deep Causal Estimation
Chen, R., Hu, S., Chen, Z., Zhu, S., Yu, B., Li, P., Chen, C., Huang, Y., & Hao, J. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3192363 · Cited by 4
VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building
Daum, M., Zhang, E., He, D. J., Mussmann, S., Haynes, B. M., Krishna, R., & Bałazińska, M. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3625054.3625057 · Cited by 4
3D Segmentation of Necrotic Lung Lesions in CT Images Using Self-Supervised Contrastive Learning
Liu, Y., Halek, S., Crawford, R., Persson, K., Tomaszewski, M. R., Wang, S., Baumgartner, R., Yuan, J. D., Goldmacher, G., & Chen, A. (2024) · IEEE Access · doi:10.1109/access.2024.3363637 · Cited by 4
Audio-Visual Contrastive Pre-train for Face Forgery Detection
Zhao, H., Zhou, W., Chen, D., Zhang, W., Guo, Y., Cheng, Z., Yan, P., & Yu, N. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3651311 · Cited by 5
Shi, Z., Zheng, H., & Dong, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112090 · Cited by 4
Bootstrap Masked Visual Modeling via Hard Patch Mining
Wang, H., Fan, J., Wang, Y., Song, K., Wang, T., Zhang, X., & Zhang, Z. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3557001 · Cited by 9
Cross-Modal Contrastive Masked AutoEncoder for Compressed Video Pre-Training
Li, B., Chen, J., Li, G., Zhang, D., Bao, X., & Huang, D. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3583168 · Cited by 5
A Large-Scale Analysis on Contextual Self-Supervised Video Representation Learning
Kumar, A. S., Kumar, A., Vineet, V., & Rawat, Y. S. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00072 · Cited by 4
Mask Again: Masked Knowledge Distillation for Masked Video Modeling
Li, X., He, S., Wu, J., Yu, Y., Nie, L., & Zhang, M. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612129 · Cited by 5
Kempitiya, T., Alahakoon, D., Osipov, E., Kahawala, S., & Silva, D. D. (2024) · Biomimetics · doi:10.3390/biomimetics9030175 · Cited by 4
PyTorchVideo: A Deep Learning Library for Video UnderstandingPreprint
Fan, H., Murrell, T., Wang, H., Alwala, K. V., Li, Y., Li, Y., Xiong, B., Ravi, N., Li, M., Yang, H., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.09887 · Cited by 4
Self-citationFang, P., Zhang, Y., & Lan, Z. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01458-3 · Cited by 3
Freire-Obregón, D., Barra, P., Castrillón-Santana, M., & Marsico, M. D. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18469-5 · Cited by 3
Contrasting Multi-Modal Similarity Framework for Video Scene Segmentation
Park, J., Kim, J., Seok, J., Lee, S., & Kim, J. (2024) · IEEE Access · doi:10.1109/access.2024.3370676 · Cited by 3
Enhancing motion visual cues for self-supervised video representation learning
Nie, M., Quan, Z., Ding, W., & Yang, W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106203 · Cited by 3
Mohammed, S. T., Argun, A., & Ascheid, G. (2024) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii58957.2024.10417385 · Cited by 4
Enhancing spatiotemporal predictive learning: an approach with nested attention module
Wang, S., & Ren, H. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02318-7 · Cited by 3
Self-Supervised Learning of Color Constancy
Ernst, M., López, F. M., Aubret, A., Fleming, R. W., & Triesch, J. (2024) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl61372.2024.10644375 · Cited by 3
ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning
Qing, Z., Huang, Z., Zhang, S., Tang, M., Gao, C., Jin, R., Ang, M. H., & Sang, N. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3244126 · Cited by 3
Goulão, M., & Oliveira, A. L. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230359 · Cited by 5
Long, B., Goodin, S., Kachergis, G., Marchman, V. A., Radwan, S. F., Sparks, R. Z., Xiang, V., Zhuang, C., Hsu, O., Newman, B., et al. (2022) · doi:10.31234/osf.io/238jk · Cited by 3
A Large-scale Analysis of Athletes’ Cumulative Race Time in Running Events
Freire-Obregón, D., Lorenzo-Navarro, J., Santana, O. J., Hernández-Sosa, D., & Castrillón-Santana, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43148-7_24 · Cited by 2
Multi-View Radar Autoencoder for Self-Supervised Automotive Radar Representation Learning
Zhu, H., He, H., Choromanska, A., Ravindran, S., Shi, B., & Chen, L. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588463 · Cited by 2
Wang, W.-C., Coninck, S. D., Leroux, S., & Simoens, P. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1490718 · Cited by 2
SSIM: self-supervised learning method based on spatially selected shifts and irregular image masking
Shao, Y., Wang, Z., & Wang, L. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07013-3 · Cited by 2
Exploring Video-Based Driver Activity Recognition under Noisy Labels
Fan, L., Wen, D., Peng, K., Yang, K., Zhang, J., Liu, R., Chen, Y., Zheng, J., Wu, J., Han, X., et al. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342850 · Cited by 2
Self-Supervised Video Representation Learning in a Heuristic Decoupled Perspective
Song, Z., Qiang, W., Zheng, C., Xiong, H., & Hua, G. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02785-4 · Cited by 2
Petti, D. J., Li, C., & Liu, N. (2026) · Plant Phenomics · doi:10.1016/j.plaphe.2026.100193 · Cited by 2
Temporal Contrastive Learning with Curriculum
Roy, S., & Etemad, A. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096811 · Cited by 2
Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play
Schaumlöffel, T., Aubret, A., Roig, G., & Triesch, J. (2023) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl55364.2023.10364409 · Cited by 2
Spatio-Temporal Crop Aggregation for Video Representation Learning
Sameni, S., Jenni, S., & Favaro, P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00521 · Cited by 2
Enhancing Sound Source Localization via False Negative Elimination
Song, Z., Zhang, J., Wang, Y., Fan, J., & Zhang, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3444029 · Cited by 2
Learning by Aligning 2D Skeleton Sequences and Multi-modality Fusion
Tran, Q.-H., Ahmed, M., Popattia, M., Ahmed, M. H., Konin, A., & Zia, M. Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72973-7_9 · Cited by 2
Less Than Few: Self-shot Video Instance Segmentation
Yang, P., Asano, Y. M., Mettes, P., & Snoek, C. G. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_26 · Cited by 2
MoQuad: Motion-focused Quadruple Construction for Video Contrastive Learning
Liu, Y., Chen, J., & Wu, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25069-9_2 · Cited by 2
Motion-aware Self-supervised Video Representation Learning via Foreground-background Merging.Preprint
Ding, S., Li, M., Yang, T., Qian, R., Xu, H., Chen, Q., & Wang, J. (2021) · arXiv (Cornell University) · Cited by 2
TCVM: Temporal Contrasting Video Montage Framework for Self-supervised Video Representation Learning
Tian, F., Fan, J., Yu, X., Du, S., Song, M., & Zhao, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26284-5_32 · Cited by 1
Qin, Y., Chu, R., & Liu, B. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001424550139 · Cited by 2
DA 2 : Distribution-agnostic adaptive feature adaptation for one-class classification
Zhang, Z., Zhao, Z., Zhang, X., & Chen, X. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104256 · Cited by 1
Çelik, A. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04887-x · Cited by 1
Qian, Y., Kargarandehkordi, A., Sun, Y., Azizian, P., Mutlu, O. C., Surabhi, S., Jabbar, Z., Wall, D. P., Washington, P. Y., & Chen, H. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00312 · Cited by 1
Sun, J., & Mahoor, M. H. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-11949-9 · Cited by 1
BIMM: Brain-Inspired Masked Modeling for Video Representation Learning
Zhang, J., Wan, Z., Nie, S., Li, C., Shan, S., & Chen, X. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3683701 · Cited by 1
SAND: A Storage Abstraction for Video-based Deep Learning
Hong, U., Lim, H., Yeo, H., Park, J., & Han, D. (2023) · ACM Workshop on Hot Topics in Storage and File Systems (HotStorage) · doi:10.1145/3599691.3603407 · Cited by 1
Cross-modal Manifold Cutmix for Self-supervised Video Representation Learning
Das, S., & Ryoo, M. S. (2023) · doi:10.23919/mva57639.2023.10216260 · Cited by 1
Fine-grained Key-Value Memory Enhanced Predictor for Video Representation Learning
Li, X., Wu, J., He, S., Kang, S., Yu, Y., Nie, L., & Zhang, M. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612131 · Cited by 1
Meta-Learning and Self-Supervised Pretraining for Storm Event Imagery Translation
Rugina, I., Dangovski, R., Veillette, M., Khorrami, P., Cheung, B., Simek, O., & Marin Soljacic, A. (2023) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec58863.2023.10363448 · Cited by 1
Self-supervised learning for interventional image analytics: toward robust device trackers
Islam, S., Murthy, V. N., Neumann, D., Das, B. K., Sharma, P., Maier, A., Comaniciu, D., & Ghesu, F. C. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.3.035001 · Cited by 1
Data Collection-Free Masked Video Modeling
Ishikawa, Y., Kondo, M., & Aoki, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73247-8_3 · Cited by 1
Pre-training for Action Recognition with Automatically Generated Fractal Datasets
Svyezhentsev, D., Retsinas, G., & Maragos, P. A. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02420-8 · Cited by 1
KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder
Bora, M., Atreya, S., Mukherjee, A., & Das, A. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00731 · Cited by 1
Inter-intra Variant Dual Representations forSelf-supervised Video RecognitionPreprint
Zhang, L., She, Q., Shen, Z., & Wang, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.01194 · Cited by 1
Video Quality Assessment-Jointed Video ClassificationPreprint
Sun, J., & Mahoor, M. H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5174457 · Cited by 1
Contrastive Learning of Image Representations with Cross-Video Cycle-ConsistencyPreprint
Wu, H., & Wang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.06463
The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation LearningPreprint
Al-Tahan, H., & Mohsenzadeh, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07082
Singhania, D., Rahaman, R., & Yao, A. (2021) · arXiv (Cornell University)
Temporal-Invariant Video Representation Learning with Dynamic Temporal Resolutions
Jeong, S.-Y., Kim, H., Oh, M., Lee, G., & Lee, S. (2022) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss56176.2022.9959310
Involving Density Prior for 3D Point Cloud Contrastive Learning
Long, F., & Qiu, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37660-3_21
Video Self-Supervised Cross-Pathway Training Based on Slow and Fast Pathways
Li, J., Yang, J. Y., Jiang, Z., Chen, Y., & Du, S. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10393923
Clustering-based multi-featured self-supervised learning for human activities and video retrieval
Javed, M. H., Zeng, Y., Rajeh, T. M., Rafique, F., & Li, T. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05460-8
Unsupervised Feature Learning for Video Understanding
Wu, Z., & Jiang, Y. (2024) · Wireless networks · doi:10.1007/978-3-031-57679-9_6
Wu, Z., & Jiang, Y. (2024) · Wireless networks · doi:10.1007/978-3-031-57679-9_7
Learning Object Semantic Similarity with Self-Supervision
Aubret, A., Schaumlöffel, T., Roig, G., & Triesch, J. (2024) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl61372.2024.10644930
Metadata
- Authors
- Feichtenhofer, C., Fan, H., Xiong, B., Girshick, R., & He, K.
- Affiliations
- Meta (United States) (US)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 3298-3308
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3170837227
- Cited by
- 222
- References
- 104
- Citations by year
- 2026: 10 · 2025: 33 · 2024: 49 · 2023: 67 · 2022: 49 · 2021: 13
- FWCI
- 14.56
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- unsupervised feature learning, spatio-temporal representation learning, SlowFast, video understanding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
