arXiv:2606.07433v1 Announce Type: cross Abstract: Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video scenarios. These scenarios require models to handle sparse evidence, long-range dependencies, multimodal alignment, and reliable inference under limited computational budgets. This work presents a human-view perspective on LLM-based video understanding, organized around three functional abilities: watching, remembering, and reasoning. Rather than treating video tasks
The rapid advancement of MLLMs is pushing research into more complex video understanding scenarios, necessitating new architectural paradigms like the human-view approach.
Improved video understanding by MLLMs could unlock new capabilities in automation, surveillance, and human-computer interaction, impacting various industries and operational efficiencies.
MLLMs are moving beyond short clips to handle long, multimodal, and knowledge-intensive video through methods that mimic human 'watching, remembering, and reasoning'.
- · AI developers
- · Surveillance technology
- · Robotics
- · Content analysis platforms
- · Tasks requiring manual video review
- · Traditional video analytics methods
- · Low-compute edge devices (initially)
More sophisticated and autonomous AI systems capable of comprehensive video interpretation and decision-making.
Increased demand for computational resources and specialized hardware to support advanced MLLM video processing.
Potential ethical and privacy debates surrounding the capabilities of AI to interpret complex human activities from video.
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Read at arXiv cs.AI