Papers › Evolving Losses for Unsupervised Video Representation Learning
Evolving Losses for Unsupervised Video Representation Learning
AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo
We present a new method to learn video representations from large-scale unlabeled video data. Ideally, this representation will be generic and transferable, directly usable for new tasks such as action recognition and zero or few-shot learning. We formulate unsupervised representation learning as a multi-modal, multi-task learning problem, where the representations are shared across different modalities via distillation. Further, we introduce the concept of loss function evolution by using an evolutionary search algorithm to automatically find optimal combination of loss functions capturing many (self-supervised) tasks and modalities. Thirdly, we propose an unsupervised representation evaluation metric using distribution matching to a large unlabeled dataset as a prior constraint, based on Zipf's law. This unsupervised constraint, which is not guided by any labeling, produces similar results to weakly-supervised, task-specific ones. The proposed unsupervised representation learning results in a single RGB network and outperforms previous methods. Notably, it is also more effective than several label-based methods (e.g., ImageNet), with the exception of large, fully labeled video datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Self-Supervised Action Recognition | HMDB51 | ELo | Frozen | false | #19 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | ELo | Top-1 Accuracy | 64.5 | #19 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 (finetuned) | ELo | Top-1 Accuracy | 67.4 | #6 of 14 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 (finetuned) | ELo | 3-fold Accuracy | 93.8 | #4 of 14 | 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.
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