Forget, Anticipate and Adapt: Test Time Training for Long Videos
Test Time Training (TTT) is a mechanism in which a model adapts to an incoming test-sample by performing some self-supervised (SSL) task and updating its weights even during inference. This procedure does not require labels at test-time. This paper focuses on TTT for long-videos. A major concern with existing approaches is: 1) they perform TTT updates using a sliding window containing frames in the past, whose compute increases linearly with the size of window. This becomes computationally intractable when the videos are hours long. 2) TTT is performed even when temporally close frames look similar, thereby consuming a lot of compute. We present the Frame Forgetting Network (FFN) that: 1)...
| Authors | Rajat Modi, Sebastian Noel, Xin Liang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 June 2026 |
| Last updated at source | 10 July 2026 |
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