Language Repository for Long Video Understanding

• URL: http://arxiv.org/abs/2403.14622v1
• Date: Thu, 21 Mar 2024 17:59:35 GMT
• Title: Language Repository for Long Video Understanding
• Authors: Kumara Kahatapitiya, Kanchana Ranasinghe, Jongwoo Park, Michael S. Ryoo,
• Abstract summary: This paper introduces a Language Repository (LangRepo) for multi-modal vision LLMs. Our repository maintains concise and structured information as an interpretable (i.e., all-textual) representation.
• Score: 41.17102343915504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language has become a prominent modality in computer vision with the rise of multi-modal LLMs. Despite supporting long context-lengths, their effectiveness in handling long-term information gradually declines with input length. This becomes critical, especially in applications such as long-form video understanding. In this paper, we introduce a Language Repository (LangRepo) for LLMs, that maintains concise and structured information as an interpretable (i.e., all-textual) representation. Our repository is updated iteratively based on multi-scale video chunks. We introduce write and read operations that focus on pruning redundancies in text, and extracting information at various temporal scales. The proposed framework is evaluated on zero-shot visual question-answering benchmarks including EgoSchema, NExT-QA, IntentQA and NExT-GQA, showing state-of-the-art performance at its scale. Our code is available at https://github.com/kkahatapitiya/LangRepo.

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