LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models
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, surveillance footage, egocentric recordings, and animated
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- Linked via arxiv authorArpita Nema →
LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models
- Linked via arxiv authorHanwei Zhu →
LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models
- Linked via arxiv authorXi Zhang →
LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models
- Linked via arxiv authorWeisi Lin →
LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models