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CapMem: A Benchmark for Caption-Based Episodic Memory in Egocentric Video
Wearable assistants require episodic memory over egocentric video, yet current vision-language models face bounded frame budgets, growing visual-token costs, and long-context retrieval failures. Under these practical constraints, we study whether textual captions can serve as reusable episodic memory. We define the Episodic Memory Video Caption QA task and introduce CapMem, a human-annotated benchmark with 75 videos totaling 33.7 hours, and 1,000 multiple-choice questions across 16 scenarios. On long videos (>20 min), full-coverage CaptionQA with 30s and 60s caption windows outperforms direct
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T18:03:04.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.