FIOVA: A Multi-Annotator Benchmark for Human-Aligned Video Captioning

• URL: http://arxiv.org/abs/2410.15270v2
• Date: Mon, 19 May 2025 15:28:21 GMT
• Title: FIOVA: A Multi-Annotator Benchmark for Human-Aligned Video Captioning
• Authors: Shiyu Hu, Xuchen Li, Xuzhao Li, Jing Zhang, Yipei Wang, Xin Zhao, Kang Hao Cheong,
• Abstract summary: We introduce FIOVA, a human-centric benchmark tailored for evaluation of large vision-language models (LVLMs)<n>It comprises 3,002 real-world videos (about 33.6s each), each annotated independently by five annotators.<n>We propose FIOVA-DQ, an event-level evaluation metric that incorporates cognitive weights derived from annotator consensus.
• Score: 15.363132825156477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite rapid progress in large vision-language models (LVLMs), existing video caption benchmarks remain limited in evaluating their alignment with human understanding. Most rely on a single annotation per video and lexical similarity-based metrics, failing to capture the variability in human perception and the cognitive importance of events. These limitations hinder accurate diagnosis of model capabilities in producing coherent, complete, and human-aligned descriptions. To address this, we introduce FIOVA (Five-In-One Video Annotations), a human-centric benchmark tailored for evaluation. It comprises 3,002 real-world videos (about 33.6s each), each annotated independently by five annotators. This design enables modeling of semantic diversity and inter-subjective agreement, offering a richer foundation for measuring human-machine alignment. We further propose FIOVA-DQ, an event-level evaluation metric that incorporates cognitive weights derived from annotator consensus, providing fine-grained assessment of event relevance and semantic coverage. Leveraging FIOVA, we conduct a comprehensive evaluation of nine representative LVLMs and introduce a complexity-aware analysis framework based on inter-annotator variation (CV). This reveals consistency gaps across difficulty levels and identifies structural issues such as event under-description and template convergence. Our results highlight FIOVA's diagnostic value for understanding LVLM behavior under varying complexity, setting a new standard for cognitively aligned evaluation in long-video captioning. The benchmark, annotations, metric, and model outputs are publicly released to support future evaluation-driven research in video understanding. More detailed information can be found at https://huuuuusy.github.io/fiova/.

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