Interpreting Video Representations with Spatio-Temporal Sparse Autoencoders
We present the first systematic study of Sparse Autoencoders (SAEs) on video representations. Standard SAEs decompose video into interpretable, monosemantic features but destroy temporal coherence: hard TopK selection produces unstable feature assignments across frames, reducing autocorrelation by 36%. We propose spatio-temporal contrastive objectives and Matryoshka hierarchical grouping that recover and even exceed raw temporal coherence. The contrastive loss weight controls a tunable trade-off between reconstruction and temporal coherence. A systematic ablation on two backbones and two datasets shows that different configurations excel at different goals: reconstruction fidelity, tempor...
| Authors | Atahan Dokme, Sriram Vishwanath |
|---|---|
| Primary category | cs.CV |
| Published | 5 April 2026 |
| Last updated at source | 7 August 2026 |
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