[Submitted on 15 Sep 2026]
Title:CapMem: A Benchmark for Caption-Based Episodic Memory in Egocentric Video
View a PDF of the paper titled CapMem: A Benchmark for Caption-Based Episodic Memory in Egocentric Video, by Dingli Liang and 8 other authors
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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.
Comments: EMNLP 2026
Subjects:
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.17688 [cs.AI]
(or arXiv:2609.17688v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.17688
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Dingli Liang [view email] [v1] Tue, 15 Sep 2026 18:03:04 UTC (2,044 KB)
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