arXiv:2607.01086v1 Announce Type: cross Abstract: The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs). Existing video quality benchmarks predominantly focus on short clips and isolated distortions, overlooking the temporal continuity, cumulative degradation, and reasoning complexity inherent in long-duration content. To address these limitations, we present LongVQUBench, a comprehensive benchmark for long-term video quality understanding. LongVQUBench contains over 1200 diverse videos spanning movies, documentaries, surveil
The rapid advancement and widespread deployment of large vision-language models necessitate more robust evaluation methodologies to understand their capabilities and limitations in real-world scenarios, particularly concerning long-form content.
This benchmark addresses a critical gap in evaluating Vision-Language Models (VLMs) by focusing on long-term video quality understanding, moving beyond short clips to assess temporal continuity and complex reasoning, which are crucial for practical applications.
The introduction of LongVQUBench will drive VLM development towards better performance on extended video understanding, leading to more reliable and sophisticated applications in areas like surveillance, media analysis, and autonomous systems.
- · VLM Developers
- · AI Research Institutions
- · Video Content Platforms
- · Surveillance Technology Providers
- · VLMs optimized only for short-form content
- · Legacy video analysis tools
VLMs will improve their ability to understand and process long-duration video content more effectively, leading to higher quality outputs across various applications.
Enhanced long-term video understanding could enable more sophisticated AI agents for monitoring, content moderation, and summarization of extensive video feeds.
This could accelerate the development of autonomous systems requiring continuous, high-fidelity environmental assessment over extended periods, potentially impacting areas like autonomous vehicles and robotic monitoring.
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Read at arXiv cs.AI