Abstract
Long videos contain a vast amount of information, making video-text retrieval an essential and challenging task in multimodal learning. However, existing benchmarks suffer from limited video duration, low-quality captions, and coarse annotation granularity, which hinder the evaluation of advanced video-text retrieval methods. To address these limitations, we introduce LoVR, a benchmark specifically designed for long video-text retrieval. LoVR contains 467 long videos and over 40,804 fine-grained clips with high-quality captions. To overcome the issue of poor machine-generated annotations, we propose an efficient caption generation framework that integrates VLM automatic generation, caption quality scoring, and dynamic refinement. This pipeline improves annotation accuracy while maintaining scalability. Furthermore, we introduce a semantic fusion method to generate coherent full-video captions without losing important contextual information. Our benchmark introduces longer videos, more detailed captions, and a larger-scale dataset, presenting new challenges for video understanding and retrieval. Extensive experiments on various advanced embedding models demonstrate that LoVR is a challenging benchmark, revealing the limitations of current approaches and providing valuable insights for future research. We release the code and dataset link at this https URL
Abstract (translated)
长时间视频包含大量的信息，使得视频-文本检索成为多模态学习中一个既重要又具挑战性的任务。然而，现有的基准测试由于视频时长有限、字幕质量低以及标注精细度不足等问题，在评估先进的视频-文本检索方法方面存在局限性。为解决这些问题，我们引入了LoVR这一专门用于长时间视频-文本检索的基准。 LoVR包含467段长时间视频和超过40,804个高质量细粒度片段。为了克服机器生成注释质量不佳的问题，我们提出了一种高效的字幕生成框架，该框架结合了视觉语言模型自动生成功能、字幕质量评分以及动态优化方法。这种流程在保证准确性的同时还保持了可扩展性。此外，我们引入了一个语义融合的方法来生成连贯的全视频描述而不丢失重要的上下文信息。 我们的基准测试提供了更长的视频、更为详细的注释和更大规模的数据集，为视频理解和检索带来了新的挑战。在各种先进的嵌入模型上的广泛实验表明，LoVR是一个具有挑战性的基准测试，揭示了现有方法的局限性，并为未来研究提供了有价值的见解。我们可以在这里（请将“this https URL”替换为实际链接）提供代码和数据集的访问。 通过这些改进，LoVR旨在促进视频-文本检索领域的发展，推动该领域的模型性能进一步提升。
URL
https://arxiv.org/abs/2505.13928
