LATex: Leveraging Attribute-based Text Knowledge for Aerial-Ground Person Re-Identification

• URL: http://arxiv.org/abs/2503.23722v1
• Date: Mon, 31 Mar 2025 04:47:05 GMT
• Title: LATex: Leveraging Attribute-based Text Knowledge for Aerial-Ground Person Re-Identification
• Authors: Xiang Hu, Yuhao Wang, Pingping Zhang, Huchuan Lu,
• Abstract summary: We propose a novel framework named LATex for AG-ReID.<n>It adopts prompt-tuning strategies to leverage attribute-based text knowledge.<n>Our framework can fully leverage attribute-based text knowledge to improve the AG-ReID.
• Score: 63.07563443280147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aerial-Ground person Re-IDentification (AG-ReID) aims to retrieve specific persons across heterogeneous cameras in different views. Previous methods usually adopt large-scale models, focusing on view-invariant features. However, they overlook the semantic information in person attributes. Additionally, existing training strategies often rely on full fine-tuning large-scale models, which significantly increases training costs. To address these issues, we propose a novel framework named LATex for AG-ReID, which adopts prompt-tuning strategies to leverage attribute-based text knowledge. More specifically, we first introduce the Contrastive Language-Image Pre-training (CLIP) model as the backbone, and propose an Attribute-aware Image Encoder (AIE) to extract global semantic features and attribute-aware features. Then, with these features, we propose a Prompted Attribute Classifier Group (PACG) to generate person attribute predictions and obtain the encoded representations of predicted attributes. Finally, we design a Coupled Prompt Template (CPT) to transform attribute tokens and view information into structured sentences. These sentences are processed by the text encoder of CLIP to generate more discriminative features. As a result, our framework can fully leverage attribute-based text knowledge to improve the AG-ReID. Extensive experiments on three AG-ReID benchmarks demonstrate the effectiveness of our proposed LATex. The source code will be available.

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