HLFormer: Enhancing Partially Relevant Video Retrieval with Hyperbolic
  Learning
Authors: name: Li Jun, name: Wang Jinpeng, name: Tan Chaolei, name: Lian Niu, name: Chen Long, name: Zhang Min, name: Wang Yaowei, name: Xia Shu-Tao, name: Chen Bin

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.