Unifying Video Tasks via Spatiotemporal Analogy
Adapting video models to new tasks typically requires dedicated data curation and fine-tuning. While visual analogy provides a training-free alternative by specifying tasks in-context, it remains restricted to the image domain. To explore whether analogy-based methods can unify diverse video tasks and generalize to out-of-distribution scenarios, we introduce ViGeo, a framework that extends visual in-context learning to the video domain via spatiotemporal canvas completion. Evaluated on a diverse task taxonomy with a strict train-test split, ViGeo generalizes to unseen video manipulations and zero-shot modalities (e.g., event cameras). Finally, we identify task internalization, where a que...
| Authors | Chia-Hsiang Kao, Belinda Zeng, Bharath Hariharan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 September 2026 |
| Last updated at source | 30 September 2026 |
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