arXiv:2603.18558v2 Announce Type: replace-cross Abstract: Long-form video question answering requires reasoning over extended temporal contexts, making frame selection a critical bottleneck for multi-modal large language models (MLLMs) bound by finite context windows. Within the controlled frame-budget regime that governs practical deployment, prior selectors score frames against a single global query embedding; as a result, compositional multimodal questions that involve temporal ordering or cross-modal cues such as ``what happens on screen right after the narrator mentions the reaction?'' ar
The proliferation of long-form video content and the growing capabilities of multimodal large language models are pushing the boundaries for efficient video understanding and reasoning.
Improving multimodal LLM efficiency in handling extensive video data reduces computational costs and enhances their ability to perform complex, nuanced analysis over time.
This advancement changes how MLLMs process long videos, moving from rudimentary frame selection to a more intelligent, hierarchical approach for understanding temporal and compositional cues.
- · AI researchers (multimodal)
- · Video analytics platforms
- · Content moderation services
- · Generative AI model developers
- · Computational resource-constrained MLLM deployments
- · Simplistic frame selection methodologies
Enhanced ability for AI to comprehend and respond to complex queries directly from long-form video content.
Accelerated development of AI applications requiring deep temporal reasoning in fields like security, education, and entertainment.
The creation of new classes of video-centric AI assistants capable of advanced reasoning over user-generated and professional media.
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Read at arXiv cs.AI