SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
When discussing the Aerial-Ground Person Re-identification (AGPReID) task, we
face the main challenge of the significant appearance variations caused by
different viewpoints, making identity matching difficult. To address this
issue, previous methods attempt to reduce the differences between viewpoints by
critical attributes and decoupling the viewpoints. While these methods can
mitigate viewpoint differences to some extent, they still face two main issues:
(1) difficulty in handling viewpoint diversity and (2) neglect of the
contribution of local features. To effectively address these challenges, we
design and implement the Self-Calibrating and Adaptive Prompt (SeCap) method
for the AGPReID task. The core of this framework relies on the Prompt
Re-calibration Module (PRM), which adaptively re-calibrates prompts based on
the input. Combined with the Local Feature Refinement Module (LFRM), SeCap can
extract view-invariant features from local features for AGPReID. Meanwhile,
given the current scarcity of datasets in the AGPReID field, we further
contribute two real-world Large-scale Aerial-Ground Person Re-Identification
datasets, LAGPeR and G2APS-ReID. The former is collected and annotated by us
independently, covering $4,231$ unique identities and containing $63,841$
high-quality images; the latter is reconstructed from the person search dataset
G2APS. Through extensive experiments on AGPReID datasets, we demonstrate that
SeCap is a feasible and effective solution for the AGPReID task. The datasets
and source code available on https://github.com/wangshining681/SeCap-AGPReID.
