HLFormer: Enhancing Partially Relevant Video Retrieval with Hyperbolic
  Learning
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.
688358b06d99b948a364d1b1
Li Jun
claimed_verified
2025-07-25T13:36:14.709Z
688206eb4f947b9971eacc7a
/avatars/4b9238add0bef154aef9401b88b4a231.svg
Jun Li
user
JunLi2005
688358b06d99b948a364d1b2
Wang Jinpeng
688358b06d99b948a364d1b3
Tan Chaolei
688358b06d99b948a364d1b4
Lian Niu
688358b06d99b948a364d1b5
Chen Long
688358b06d99b948a364d1b6
Zhang Min
688358b06d99b948a364d1b7
Wang Yaowei
688358b06d99b948a364d1b8
Xia Shu-Tao
688358b06d99b948a364d1b9
Chen Bin
HLFormer: Enhancing Partially Relevant Video Retrieval with Hyperbolic
  Learning
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.
HLFormer, a hyperbolic modeling framework, improves video retrieval by integrating Lorentz and Euclidean attention blocks and enforcing partial order preservation in hybrid spaces.
hyperbolic modeling HLFormer Lorentz Attention Block Euclidean Attention Block Mean-Guided Adaptive Interaction Module Partial Order Preservation Loss Lorentzian cone constraints cross-modal matching