Papers › Self-supervised Video Transformer

Self-supervised Video Transformer

2 Dec 2021CVPR 2022 1arXiv:2112.01514archive 2025-07-28

Kanchana Ranasinghe, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, Michael Ryoo

In this paper, we propose self-supervised training for video transformers using unlabeled video data. From a given video, we create local and global spatiotemporal views with varying spatial sizes and frame rates. Our self-supervised objective seeks to match the features of these different views representing the same video, to be invariant to spatiotemporal variations in actions. To the best of our knowledge, the proposed approach is the first to alleviate the dependency on negative samples or dedicated memory banks in Self-supervised Video Transformer (SVT). Further, owing to the flexibility of Transformer models, SVT supports slow-fast video processing within a single architecture using dynamically adjusted positional encoding and supports long-term relationship modeling along spatiotemporal dimensions. Our approach performs well on four action recognition benchmarks (Kinetics-400, UCF-101, HMDB-51, and SSv2) and converges faster with small batch sizes. Code: https://git.io/J1juJ

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Tasks

Action ClassificationAction RecognitionAction Recognition In VideosSelf-Supervised Action Recognition Linear

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Classification Kinetics-400 SVT Acc@1 78.1 #129 of 207 Archive leaderboard report
Action Recognition HMDB-51 SVT Average accuracy of 3 splits 67.2 #58 of 77 Archive leaderboard report
Action Recognition Something-Something V2 SVT Top-1 Accuracy 59.2 #114 of 123 Archive leaderboard report
Action Recognition UCF101 SVT 3-fold Accuracy 93.7 #58 of 91 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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