arXiv:2606.05917v1 Announce Type: cross Abstract: Long-video question answering remains challenging for Vision-Language Models (VLMs), as answer-relevant evidence is often sparse, transient, and temporally dispersed across lengthy video contexts. Existing frame-centric approaches improve efficiency through uniform sampling, query-aware frame selection, visual-token compression, and adaptive resolution strategies. However, they still rely on isolated and fragmented frames as the fundamental evidence units, limiting VLMs' ability to effectively capture coherent event-level semantics. To address
This research addresses a fundamental limitation in current Vision-Language Models (VLMs) as they strive to process and understand increasingly longer video content, a common format across many applications.
Improving long-video question answering can significantly enhance the capabilities of AI systems in fields like surveillance, content generation, education, and entertainment, leading to more sophisticated and autonomous applications.
The proposed 'MemoryCard' approach shifts from fragmented frame analysis to topic-aware, multi-modal clue compression, allowing VLMs to capture coherent event-level semantics more effectively.
- · AI developers
- · Video analytics companies
- · Content platforms
- · Robotics and autonomous systems
- · Current frame-centric VLM architectures
- · Companies relying on inefficient video processing
VLMs will become more adept at understanding complex narratives and events within lengthy video sequences.
This improved understanding could lead to more robust AI agents capable of operating in dynamic, video-rich environments.
Enhanced video comprehension might accelerate the development of advanced monitoring, learning, and interaction systems, blurring the lines between human and AI perception.
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Read at arXiv cs.CL