What really matters for person re-identification? A Mixture-of-Experts Framework for Semantic Attribute Importance

• URL: http://arxiv.org/abs/2512.08697v1
• Date: Tue, 09 Dec 2025 15:14:28 GMT
• Title: What really matters for person re-identification? A Mixture-of-Experts Framework for Semantic Attribute Importance
• Authors: Athena Psalta, Vasileios Tsironis, Konstantinos Karantzalos,
• Abstract summary: MoSAIC-ReID is a Mixture-of-Experts framework that systematically quantifies the importance of pedestrian attributes for re-identification.<n>Our approach uses LoRA-based experts, each linked to a single attribute, and an oracle router that enables controlled attribution analysis.
• Score: 3.1485041255193784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art person re-identification methods achieve impressive accuracy but remain largely opaque, leaving open the question: which high-level semantic attributes do these models actually rely on? We propose MoSAIC-ReID, a Mixture-of-Experts framework that systematically quantifies the importance of pedestrian attributes for re-identification. Our approach uses LoRA-based experts, each linked to a single attribute, and an oracle router that enables controlled attribution analysis. While MoSAIC-ReID achieves competitive performance on Market-1501 and DukeMTMC under the assumption that attribute annotations are available at test time, its primary value lies in providing a large-scale, quantitative study of attribute importance across intrinsic and extrinsic cues. Using generalized linear models, statistical tests, and feature-importance analyses, we reveal which attributes, such as clothing colors and intrinsic characteristics, contribute most strongly, while infrequent cues (e.g. accessories) have limited effect. This work offers a principled framework for interpretable ReID and highlights the requirements for integrating explicit semantic knowledge in practice. Code is available at https://github.com/psaltaath/MoSAIC-ReID

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