SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks

2025-03-10

AI Summary

To address the severe appearance variations and identity matching challenges in aerial-ground cross-view person re-identification (AGPReID) caused by drastic viewpoint differences, this paper proposes Self-Calibrating and Adaptive Prompting (SeCap). SeCap introduces a novel Prompt Re-calibration Module (PRM) to achieve viewpoint-robust global representation learning, and a Local Feature Refinement Module (LFRM) to enhance discriminative local detail modeling. The method integrates vision transformer-based prompt learning, adaptive feature recalibration, local feature disentanglement, and cross-view alignment. We construct two large-scale real-world benchmarks—LAGPeR (4,231 identities, 63,841 images) and G2APS-ReID—and publicly release both datasets and source code. Extensive experiments demonstrate that SeCap significantly outperforms state-of-the-art methods across multiple AGPReID benchmarks.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Privacy-enhancing technologies

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.

Problem

• Addresses viewpoint variations in aerial-ground person re-identification.
• Improves handling of viewpoint diversity and local feature extraction.
• Introduces new datasets to tackle scarcity in AGPReID research.

Innovation

• Self-Calibrating and Adaptive Prompt (SeCap) method
• Prompt Re-calibration Module (PRM) for adaptive prompts
• Local Feature Refinement Module (LFRM) for view-invariant features