EgoSAT: A Comprehensive Benchmark of Egocentric Streaming Interaction Understanding
We introduce EgoSAT, the first comprehensive benchmark for egocentric video reasoning in streaming settings, designed to evaluate the capabilities of modern vision-language models (VLMs). The benchmark targets streaming interaction understanding, where video frames arrive sequentially and models must continuously interpret evolving visual context. EgoSAT unifies several previously distinct tasks within a single streaming framework. In this formulation, queries about completed events correspond to retrospective reasoning, queries about ongoing activities require online understanding, and queries about future actions involve prospective anticipation. This unified setting requires models to ...
| Authors | Yijia Lei, Jinzhao Li, Yichi Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
Connected in the knowledge graph
- YijiaLeithu/EgoSAT-ROI-CacheDataset · cited by
- YijiaLeithu/EgoSATDataset · cited by
Models and datasets referencing this paper
- YijiaLeithu/EgoSATDataset
- YijiaLeithu/EgoSAT-ROI-CacheDataset
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