Pixel-level Scene Understanding in One Token: Visual States Need What-is-Where Composition
For robotic agents operating in dynamic environments, learning visual state representations from streaming video observations is essential for sequential decision making. Recent self-supervised learning methods have shown strong transferability across vision tasks, but they do not explicitly address what a good visual state should encode. We argue that effective visual states must capture what-is-where by jointly encoding the semantic identities of scene elements and their spatial locations, enabling reliable detection of subtle dynamics across observations. To this end, we propose CroBo, a visual state representation learning framework based on a global-to-local reconstruction objective....
| Authors | Seokmin Lee, Yunghee Lee, Byeonghyun Pak et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 March 2026 |
| Last updated at source | 25 March 2026 |
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