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arXiv · 2609.17688

CapMem: A Benchmark for Caption-Based Episodic Memory in Egocentric Video

Dingli Liang·Yiqiao Xie·Yukai Huang·Zhaokai Wang·Weitong Cai·Guangwen Feng·Jifei Song·Zhensong Zhang·Hang Zhang
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Abstract

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 VideoQA for 10/12 and 8/12 models, respectively. On the same video subset, a matched-frame control across six Qwen models retains mean accuracy gains of 3.22 and 2.55 points, respectively. Our caption-guided retrieve-and-verify harness further improves accuracy by up to 5.3 points. These results support the effectiveness of caption memory for episodic reasoning over long egocentric video.

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Dingli Liang, Yiqiao Xie, Yukai Huang, Zhaokai Wang, Weitong Cai, Guangwen Feng, Jifei Song, Zhensong Zhang, Hang Zhang. 2026-09-15. CapMem: A Benchmark for Caption-Based Episodic Memory in Egocentric Video. https://arxiv.org/abs/2609.17688

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