Raffaele Cali, Giuseppe Lando, Rosario Forte, Antonino Furnari · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31284 · open-access PDF
长自私的视频对多式语言模型构成了一个核心挑战:视觉流必须被压缩成一个可以有效地搜索的记忆表示,同时仍然保留了回答问题所需的证据。 基于一个在线文本记忆框架,其中第一人称视频被转换为时间地标,这项工作系统地研究了检索在 episodic-memory 视频问答中的作用。
Machine translation (Meta M2M100); the English original follows.
Long egocentric videos pose a central challenge for multimodal language models: the visual stream must be compressed into a memory representation that can be searched efficiently while still preserving the evidence needed for question answering. Building on an online textual-memory framework in which first-person videos are converted into temporally grounded captions, this work systematically studies the role of retrieval in episodic-memory video question answering. We evaluate how different retrieval units, query formulations, context budgets, and reranking strategies affect multiple-choice a
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