Audio-Visual LLM for Video Understanding

• URL: http://arxiv.org/abs/2312.06720v2
• Date: Wed, 13 Dec 2023 04:45:01 GMT
• Title: Audio-Visual LLM for Video Understanding
• Authors: Fangxun Shu, Lei Zhang, Hao Jiang, Cihang Xie
• Abstract summary: This paper presents Audio-Visual LLM, a Multimodal Large Language Model that takes both visual and auditory inputs for holistic video understanding. We introduce a high-quality video instruction dataset, derived from GPT-4. Experiments demonstrate that Audio-Visual LLM impressively achieves strong zero-shot results across a range of video understanding tasks.
• Score: 25.963166809113005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents Audio-Visual LLM, a Multimodal Large Language Model that takes both visual and auditory inputs for holistic video understanding. A key design is the modality-augmented training, which involves the integration of modality-specific tokens engineered to activate the appropriate visual and/or auditory encoder selectively. This mechanism is pivotal in enabling end-to-end joint training with video data at different modalities, including visual-only, audio-only, and audio-visual formats. Moreover, we introduce a high-quality video instruction dataset, derived from GPT-4. This dataset allows Audio-Visual LLM to adeptly process a variety of task-oriented video instructions, ranging from multi-turn conversations and audio-visual narratives to complex reasoning tasks. Extensive experiments demonstrate that Audio-Visual LLM impressively achieves strong zero-shot results across a range of video understanding tasks. For example, Audio-Visual LLM achieves an accuracy of 53.7% on MSRVTT-QA, outperforming non-LLM-based InterVideo by 6.6% and LLM-based Valley by 4.4%, respectively. Additionally, our Audio-Visual LLM also achieves competitive performance on audio tasks (e.g., AudioCaps).

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