Papers › Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization

Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization

20 Mar 2023ICCV 2023 1arXiv:2303.11003archive 2025-07-28

Fida Mohammad Thoker, Hazel Doughty, Cees Snoek

We propose a self-supervised method for learning motion-focused video representations. Existing approaches minimize distances between temporally augmented videos, which maintain high spatial similarity. We instead propose to learn similarities between videos with identical local motion dynamics but an otherwise different appearance. We do so by adding synthetic motion trajectories to videos which we refer to as tubelets. By simulating different tubelet motions and applying transformations, such as scaling and rotation, we introduce motion patterns beyond what is present in the pretraining data. This allows us to learn a video representation that is remarkably data efficient: our approach maintains performance when using only 25\% of the pretraining videos. Experiments on 10 diverse downstream settings demonstrate our competitive performance and generalizability to new domains and fine-grained actions.

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