Koala: Key frame-conditioned long video-LLM
<|reference_start|>Koala: Key frame-conditioned long video-LLM: Long video question answering is a challenging task that involves recognizing short-term activities and reasoning about their fine-grained relationships. State-of-the-art video Large Language Models (vLLMs) hold promise as a viable solution due to their demonstrated emergent capabilities on new tasks. However, despite being trained on millions of short seconds-long videos, vLLMs are unable to understand minutes-long videos and accurately answer questions about them. To address this limitation, we propose a lightweight and self-supervised approach, Key frame-conditioned long video-LLM (Koala), that introduces learnable spatiotemporal queries to adapt pretrained vLLMs for generalizing to longer videos. Our approach introduces two new tokenizers that condition on visual tokens computed from sparse video key frames for understanding short and long video moments. We train our proposed approach on HowTo100M and demonstrate its effectiveness on zero-shot long video understanding benchmarks, where it outperforms state-of-the-art large models by 3 - 6% in absolute accuracy across all tasks. Surprisingly, we also empirically show that our approach not only helps a pretrained vLLM to understand long videos but also improves its accuracy on short-term action recognition.<|reference_end|>
@article{tan2024koala:,
    title={Koala: Key frame-conditioned long video-LLM},
    author={Reuben Tan, Ximeng Sun, Ping Hu, Jui-hsien Wang, Hanieh Deilamsalehy,
  Bryan A. Plummer, Bryan Russell, Kate Saenko},
    journal={arXiv preprint arXiv:2404.04346},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04346},
    primaryClass={cs.CV}
}