Dual-Space Video Person Re-identification

Abstract

Video person re-identification (VReID) aims to recognize individuals across video sequences. Existing methods primarily use Euclidean space for representation learning but struggle to capture complex hierarchical structures, especially in scenarios with occlusions and background clutter. In contrast, hyperbolic space, with its negatively curved geometry, excels at preserving hierarchical relationships and enhancing discrimination between similar appearances. Inspired by these, we propose Dual-Space Video Person Re-Identification (DS-VReID) to utilize the strength of both Euclidean and hyperbolic geometries, capturing the visual features while also exploring the intrinsic hierarchical relations, thereby enhancing the discriminative capacity of the features. Specifically, we design the Dynamic Prompt Graph Construction (DPGC) module, which uses a pre-trained CLIP model with learnable dynamic prompts to construct 3D graphs that capture subtle changes and dynamic information in video sequences. Building upon this, we introduce the Hyperbolic Disentangled Aggregation (HDA) module, which addresses long-range dependency modeling by decoupling node distances and integrating adjacency matrices, capturing detailed spatial-temporal hierarchical relationships. Extensive experiments on benchmark datasets demonstrate the superiority of DS-VReID over state-of-the-art methods, showcasing its potential in complex VReID scenarios.