Language Repository for Long Video Understanding
<|reference_start|>Language Repository for Long Video Understanding: 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.<|reference_end|>
@article{kahatapitiya2024language,
    title={Language Repository for Long Video Understanding},
    author={Kumara Kahatapitiya, Kanchana Ranasinghe, Jongwoo Park, Michael S.
  Ryoo},
    journal={arXiv preprint arXiv:2403.14622},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14622},
    primaryClass={cs.CV}
}