Raffaele Cali, Giuseppe Lando, Rosario Forte, Antonino Furnari · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31284 · open-access PDF
Video egosentris panjang merupakan tantangan utama untuk model bahasa multimodal: aliran visual harus dikompresi menjadi representasi memori yang dapat dicari secara efisien sambil tetap mempertahankan bukti yang diperlukan untuk menjawab pertanyaan. Berdasarkan kerangka kerja teks-memori online di mana video orang pertama dikonversi menjadi kutipan yang didasarkan pada waktu, pekerjaan ini secara sistematis mempelajari peran retrieval dalam jawaban pertanyaan video episodik-memori.
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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