HLFormer: Enhancing Partially Relevant Video Retrieval with Hyperbolic Learning

TL;DR

HLFormer introduces a hyperbolic learning framework for partially relevant video retrieval, effectively capturing hierarchical semantics and improving cross-modal matching over existing Euclidean-based methods.

Contribution

This paper presents the first hyperbolic modeling approach for PRVR, integrating Lorentz and Euclidean attention blocks with a novel loss to better encode hierarchical video semantics.

Findings

HLFormer outperforms state-of-the-art PRVR methods.

Hyperbolic space modeling improves hierarchical semantic encoding.

The Partial Order Preservation Loss enhances cross-modal relevance.

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 https://github.com/lijun2005/ICCV25-HLFormer.