Understanding Long Videos in One Multimodal Language Model Pass

• URL: http://arxiv.org/abs/2403.16998v1
• Date: Mon, 25 Mar 2024 17:59:09 GMT
• Title: Understanding Long Videos in One Multimodal Language Model Pass
• Authors: Kanchana Ranasinghe, Xiang Li, Kumara Kahatapitiya, Michael S. Ryoo,
• Abstract summary: Large Language Models (LLMs) are known to contain a strong awareness of world knowledge. We propose Likelihood Selection, a technique that unlocks faster inference in autoregressive LLMs. Our resulting Multimodal Video Understanding framework demonstrates state-of-the-art performance across long-video and fine-grained action recognition benchmarks.
• Score: 44.78900245769057
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Large Language Models (LLMs), known to contain a strong awareness of world knowledge, have allowed recent approaches to achieve excellent performance on Long-Video Understanding benchmarks, but at high inference costs. In this work, we first propose Likelihood Selection, a simple technique that unlocks faster inference in autoregressive LLMs for multiple-choice tasks common in long-video benchmarks. In addition to faster inference, we discover the resulting models to yield surprisingly good accuracy on long-video tasks, even with no video specific information. Building on this, we inject video-specific object-centric information extracted from off-the-shelf pre-trained models and utilize natural language as a medium for information fusion. Our resulting Multimodal Video Understanding (MVU) framework demonstrates state-of-the-art performance across long-video and fine-grained action recognition benchmarks. Code available at: https://github.com/kahnchana/mvu

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