Abstract
Partially Relevant Video Retrieval (PRVR) addresses the critical challenge of matching untrimmed videos with text queries describing only partial content. Existing methods suffer from geometric distortion in Euclidean space that sometimes misrepresents the intrinsic hierarchical structure of videos and overlooks certain hierarchical semantics, ultimately leading to suboptimal temporal modeling. To address this issue, we propose the first hyperbolic modeling framework for PRVR, namely HLFormer, which leverages hyperbolic space learning to compensate for the suboptimal hierarchical modeling capabilities of Euclidean space. Specifically, HLFormer integrates the Lorentz Attention Block and Euclidean Attention Block to encode video embeddings in hybrid spaces, using the Mean-Guided Adaptive Interaction Module to dynamically fuse features. Additionally, we introduce a Partial Order Preservation Loss to enforce "text < video" hierarchy through Lorentzian cone constraints. This approach further enhances cross-modal matching by reinforcing partial relevance between video content and text queries. Extensive experiments show that HLFormer outperforms state-of-the-art methods. Code is released at this https URL.
Abstract (translated)
部分相关视频检索（PRVR）旨在解决未裁剪视频与仅描述部分内容的文本查询之间的匹配难题。现有方法在欧几里得空间中存在几何失真，这有时会错误地表示视频内在层次结构，并忽略某些层级语义，最终导致次优的时间建模效果。为了解决这一问题，我们提出了首个用于PRVR的双曲线建模框架HLFormer，该框架利用双曲空间学习来弥补欧几里得空间在层级建模能力上的不足。 具体来说，HLFormer结合了洛伦兹注意力块和欧氏注意力块，在混合空间中编码视频嵌入，并使用均值引导自适应交互模块动态融合特征。此外，我们引入了一个部分顺序保持损失函数，通过洛伦茨锥约束来强制执行“文本<视频”的层级关系。这种方法通过强化视频内容与文本查询之间的部分相关性进一步增强了跨模态匹配。 广泛的实验表明，HLFormer优于当前最先进的方法。代码可在以下网址获取：[此URL]（请替换为实际的URL）。
URL
https://arxiv.org/abs/2507.17402
