Abstract
Partially Relevant Video Retrieval (PRVR) is a practical yet challenging task that involves retrieving videos based on queries relevant to only specific segments. While existing works follow the paradigm of developing models to process unimodal features, powerful pretrained vision-language models like CLIP remain underexplored in this field. To bridge this gap, we propose ProPy, a model with systematic architectural adaption of CLIP specifically designed for PRVR. Drawing insights from the semantic relevance of multi-granularity events, ProPy introduces two key innovations: (1) A Prompt Pyramid structure that organizes event prompts to capture semantics at multiple granularity levels, and (2) An Ancestor-Descendant Interaction Mechanism built on the pyramid that enables dynamic semantic interaction among events. With these designs, ProPy achieves SOTA performance on three public datasets, outperforming previous models by significant margins. Code is available at this https URL.
Abstract (translated)
部分相关视频检索（PRVR）是一项实用且具有挑战性的任务，涉及根据与特定片段相关的查询来检索视频。尽管现有的研究遵循开发用于处理单一模态特征的模型的方法，但强大的预训练视觉-语言模型如CLIP在这一领域中尚未得到充分探索。为了弥合这一差距，我们提出了ProPy，这是一种专门针对PRVR设计的、对CLIP进行了系统架构适应的模型。从多粒度事件语义相关性的见解出发，ProPy引入了两个关键创新：(1) 一个提示金字塔结构，用于组织事件提示以捕捉多个粒度级别的语义；(2) 基于该金字塔构建的祖裔交互机制，使事件之间能够进行动态的语义互动。凭借这些设计，ProPy在三个公共数据集上取得了SOTA（最先进的）性能，并且显著优于之前的模型。代码可在提供的链接中获取。
URL
https://arxiv.org/abs/2508.19024
