Kolmogorov-Arnold Nonlinear Enhancement Method for Aerial-Ground Person Re-Identification

CHEN Yijun; ZENG Xianxian; LIU Shun; WANG Leijun

doi:10.11999/JEIT260430

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CHEN Yijun, ZENG Xianxian, LIU Shun, WANG Leijun. Kolmogorov-Arnold Nonlinear Enhancement Method for Aerial-Ground Person Re-Identification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260430

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CHEN Yijun, ZENG Xianxian, LIU Shun, WANG Leijun. Kolmogorov-Arnold Nonlinear Enhancement Method for Aerial-Ground Person Re-Identification[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260430

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Kolmogorov-Arnold Nonlinear Enhancement Method for Aerial-Ground Person Re-Identification

doi: 10.11999/JEIT260430 cstr: 32379.14.JEIT260430

1.
School of Artificial Intelligence, Guangdong Polytechnic Normal University, Guangzhou 510665, China
2.
School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China

Funds: The National Natural Science Foundation of China (62401164), Guangdong Basic and Applied Basic Research Foundation (2024A1515010219, 2026A1515011911), Guangdong Provincial Department of Education Key Field Project on Artificial Intelligence (2025ZDZX3008), Guangdong Key Research Institute of Humanities and Social Sciences at Universities (2025WZJD005), The Key Discipline Improvement Project of Guangdong Province (2025ZDJS023)

Received Date: 2026-04-14
Accepted Date: 2026-06-18
Rev Recd Date: 2026-06-17

Available Online: 2026-06-29

Abstract

Abstract

Objective Aerial-Ground Person Re-IDentification (AG-PReID) aims to match the same person across Unmanned Aerial Vehicle (UAV) and ground-camera views. Compared with conventional same-platform person re-identification, this task faces larger cross-view appearance variation and more severe cross-domain distribution shifts. Under these conditions, identity-consistent cues are often weakened by strong viewpoint asymmetry and cross-domain appearance distortion. Existing methods mainly focus on feature extraction and cross-view representation alignment. However, the classification supervision branch still relies heavily on linear feature transformation, which limits its ability to model complex nonlinear discriminative relationships in high-dimensional feature spaces. A stronger nonlinear supervision mapping is therefore needed to better exploit high-order feature interactions and local discriminative variations. To address this issue, this paper proposes a Kolmogorov-Arnold Nonlinear Enhancement Module (KANEM). KANEM replaces the conventional fully connected feature transformation between backbone features and the linear classifier. It uses learnable nonlinear mappings to adaptively enhance features for more discriminative cross-view representation learning. Methods The backbone follows the View-Decoupled Transformer (VDT), which introduces an additional view token and performs layer-wise view decoupling. This design separates view-related factors from identity features and reduces representation bias between aerial and ground domains. Based on this framework, KANEM replaces the conventional fully connected feature transformation between backbone features and the linear classifier, thereby providing adaptive nonlinear mappings for feature enhancement. Specifically, KANEM consists of a base activation branch and a spline branch, which are stacked into cascaded function-mapping layers. This design enables more flexible nonlinear modeling than conventional linear or MultiLayer Perceptron (MLP)-based transformations. It allows the model to capture local nonlinear variations and complex correlations among feature dimensions. To improve discriminability and further separate identity and view information, the network is jointly optimized using identity classification loss, view classification loss, triplet loss, and orthogonality loss. KANEM is used only during training and is removed during inference, so no extra inference cost is introduced. Results and Discussions Comprehensive evaluations are conducted on the CARGO and AG-ReID datasets. The results show that the proposed method consistently performs better than the baseline model and existing state-of-the-art methods. On CARGO, the proposed method achieves 70.19%/63.16%/51.34% in Rank-1 accuracy, Mean Average Precision (mAP), and Mean Inverse Negative Penalty (mINP), respectively, under the overall ALL retrieval protocol. It also achieves 58.75%/53.27%/41.11% under the most challenging aerial-ground (A↔G) cross-view retrieval protocol (Table 1). On AG-ReID, the proposed method achieves the best performance under both retrieval protocols. It reaches 84.41%/76.21%/53.05% in Rank-1/mAP/mINP for aerial-to-ground (A→G) retrieval and 86.69%/77.99%/52.28% for ground-to-aerial (G→A) retrieval (Table 2). Ablation studies on CARGO further verify the effectiveness of KANEM. They show that KANEM achieves better overall performance than conventional linear transformation and MLP-based alternatives. This result indicates that the proposed nonlinear enhancement strategy is more suitable for supervision mapping in AG-PReID (Tables 3 and 4). In addition, integrating KANEM into other person re-identification tasks further demonstrates its potential generalization ability across different scenarios (Table 5). Parameter analysis shows that setting λ to 0.001 enables the model to better balance the complexity difference between view classification and identity classification (Fig. 2a). When G and P are set to 5 and 3, respectively, the model effectively fits nonlinear variations in the feature space while preserving the smoothness and continuity of spline functions. This setting achieves effective nonlinear feature enhancement (Fig. 2(b)～(d)). The two-dimensional t-distributed Stochastic Neighbor Embedding (t-SNE) visualization shows that the enhanced features have higher intra-class compactness and better inter-class separability (Fig. 3). The top-5 retrieval comparisons further provide qualitative evidence that the proposed method improves ranking quality and retrieval robustness under all four retrieval protocols on CARGO. It promotes correct matches to higher positions and returns more relevant samples among the top-ranked results (Fig. 4). Conclusions This paper presents KANEM for AG-PReID. The proposed module is motivated by the large discrepancy between UAV and ground-camera views and by the limited capacity of linear feature transformation in the classification branch to capture complex nonlinear discriminative relationships. By replacing the conventional fully connected feature transformation between backbone output features and the linear classifier, KANEM provides a more flexible nonlinear supervision mechanism for cross-view representation learning. Through adaptive nonlinear enhancement, it better models complex feature interactions in high-dimensional spaces and strengthens the representation of cross-view consistency and fine-grained discriminative cues. Experimental results on CARGO and AG-ReID demonstrate the effectiveness of the proposed method, particularly in challenging scenarios with large view discrepancies. Future work will further refine the nonlinear mapping mechanism of KANEM and explore its use in more complex cross-view settings to improve model discriminability and generalization performance.

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