AVOC: Enhancing Hour-Level Audio-Video Understanding in Omni-Modal LLMs via Retrieval-Inspired Token Compression
arXiv:2606.24286v1 Announce Type: new Abstract: Multimodal Large Language Models have achieved remarkable progress in short-form audio-video understanding, yet long-form audio-video comprehension remains challenged by limited context windows and severe information redundancy. To address these bottlenecks, we propose AVOC, a framework for long-form audio-video understanding in Omni-modal Large Language Models. AVOC introduces a learnable token compression module between the modality encoders and the LLM backbone. We reframe multimodal token compression as a top-$K$ retrieval problem: given a fi
The proliferation of multimodal LLMs and the increasing demand for long-form content understanding necessitates innovation in context window management and information compression.
Improving long-form audio-video understanding in LLMs unlocks new applications for content analysis, surveillance, and human-computer interaction, impacting various industries that rely on interpreting extensive multimedia data.
The ability of LLMs to process and comprehend hours of audio-video data more efficiently is significantly enhanced, moving beyond short-form content limitations and enabling deeper insights from longer contexts.
- · LLM developers
- · Content analytics platforms
- · AI-powered surveillance solutions
- · Generative AI for media
- · Legacy manual content review
- · Systems with limited context window LLMs
More sophisticated and comprehensive analysis of long-form multimedia data becomes feasible.
This could lead to new forms of automated content generation, summarization, and retrieval for broadcast media and educational content.
Enhanced understanding of long human interactions could improve human-robot collaboration and empathetic AI systems.
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Read at arXiv cs.CL