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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- LinkedLinked via unknownvlm-starter →
- LinkedLinked via unknownmrreviewai/ai-tools-for-content-creators →
- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “LongVQUBench: Benchmarking Long-Term Video Quality Understan” ≈ “VioletVision-3B””
- LinkedLinked via arxiv author · 85%Arpita Nema →
“LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models”
- LinkedLinked via arxiv author · 85%Hanwei Zhu →
“LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models”
- LinkedLinked via arxiv author · 85%Xi Zhang →
“LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models”
- LinkedLinked via arxiv author · 85%Weisi Lin →
“LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models”
- PossiblePossibly related (embedding) · 48%VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization - Apple Machine Learning Research →