CapRiCorn-1K：面向视频字幕和时间尺度上主体指代一致性的综合基准
CapRiCorn-1K: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales
AI总结：
提出CapRiCorn-1K基准，评估视频字幕质量和主体指代一致性，发现现有模型在长视频中表现下降，且指标与下游任务性能强相关。
摘要：
准确且全面的视频字幕，并保持主体指代一致性，对于下游理解和生成任务至关重要。然而，现有基准很少能客观、全面地评估这些属性在不同时长和场景下的表现，从而阻碍了视频字幕模型的发展。为弥补这一空白，我们提出了CapRiCorn-1K，一个综合基准，旨在评估长时间跨度和多样化视频领域中的视频字幕质量和主体指代一致性。为适应不同的评估需求，我们的基准支持视听和仅视觉两种设置。在CapRiCorn-1K上的大量实验表明，当前模型在生成准确、全面的字幕并保持主体指代一致性方面普遍存在困难。此外，随着视频时长增加，整体字幕质量和主体指代一致性均下降。值得注意的是，我们的评估指标与基于生成字幕的下游理解和生成任务的性能表现出强相关性，进一步验证了其有效性。项目地址见https://this URL。
英文摘要：
Accurate and comprehensive video captions with consistent subject references are critical for downstream understanding and generation tasks. However, few existing benchmarks can objectively and comprehensively evaluate these properties across diverse durations and scenarios, thereby hindering the advancement of video captioning models. To bridge this gap, we propose CapRiCorn-1K, a comprehensive benchmark designed to evaluate both video captioning quality and subject referential consistency across long temporal horizons and diverse video domains. To accommodate varied evaluation needs, our benchmark supports both audiovisual and visual-only settings. Extensive experiments on CapRiCorn-1K reveal that current models generally struggle to generate accurate and comprehensive captions while maintaining consistent subject references. Moreover, as video duration increases, both the overall caption quality and subject referential consistency decline. Notably, our evaluation metrics exhibit strong correlations with the performance of downstream understanding and generation tasks conditioned on the generated captions, further validating their effectiveness. The project is available at https://github.com/xlchen0205/CapRiCorn-1K .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
