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Duoqian Miao

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Jul 2026

Mask-Guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification

Unsupervised person re-identification (ReID) aims to learn identity-discriminative representations without manual annotations, which is challenging due to noisy pseudo labels, background clutter, and large appearance variations. Recent studies have shown that exploiting fine-grained local cues is crucial for improving robustness in unsupervised ReID. In this context, random masking has emerged as a simple and annotation-free way to encourage the model to focus on informative regions. However, existing masking-based unsupervised ReID methods still suffer from two limitations: (1) Underused masked views: masked views are treated as degraded auxiliaries rather than exploited as fine-grained supervisory signals; (2) Weak cross-view alignment: feature alignment is restricted to mini-batch pairs, lacking explicit global alignment between masked and unmasked views across clusters. To address these issues, we propose the Mask-guided Asymmetric Contrastive and Semantic Alignment (ACSA) framework. Specifically, we introduce an Asymmetric Contrastive Learning (ACL) module with a dual-memory mechanism to separately encode masked and unmasked features, allowing masked views to serve as informative and discriminative supervision. In parallel, a Semantic Alignment Learning (SAL) module conducts multi-granularity distribution alignment by aligning both cluster-level prototypes and randomly sampled instance-level features, thereby preserving semantic consistency and intra-cluster diversity. Furthermore, to provide more reliable semantic anchors for SAL under noisy pseudo labels, we introduce a Progressive Refinement Module (PRM), which refines prototypes and features via exponential moving averaging for more stable semantic alignment. Extensive experiments validate the superiority of our method, even outperforming certain supervised counterparts. Code is available at https://github.com/Trangle12/ACSA

Xuan Tan, Ruijian Wei, Qixian Zhang et al. · 0 citations
Book Open access Aug 2026

HeteroFL-LoRA: Federated LoRA Fine-Tuning Across Heterogeneous LFMs via Singular Value Collaboration

Recent studies have integrated Low-Rank Adaptation (LoRA) into federated learning to enable parameter-efficient fine-tuning of Large Foundation Models (LFMs) across data silos, supporting responsible and privacy-preserving collaboration in distributed environments. However, these studies assume that all clients share the same LFM, so each client fine-tunes LoRA on private data using identical pre-trained weights and the server directly aggregates the resulting LoRA updates for collaborative training. The ideal assumption rarely holds in real-world deployments, where clients typically possess heterogeneous base LFMs. Because LoRA updates are anchored to model-specific pre-trained weights, direct aggregation is not only invalid but often counterproductive, leading to sharply degraded collaborative performance. To address this issue, we propose HeteroFL-LoRA, a federated framework that enables LoRA fine-tuning across heterogeneous LFMs. We express LoRA updates through singular value adjustments, activating task-specific knowledge in an parameter-efficient manner while decoupling them from model-specific pre-trained weights. Then, we introduce a Singular Matrix–Guided Subspace Projection that employs singular matrices to achieve cross-subspace mapping, enabling heterogeneous clients to aggregate their LoRA updates in a unified representation space. In this way, task-specific knowledge can be effectively transferred and shared across different LFMs. Experimental results demonstrate that HeteroFL-LoRA achieves effective and robust collaboration across heterogeneous LFMs while maintaining advantages in both training and communication efficiency.

Zhuojia Wu, Qi Zhang, Xuerong Zhao et al. · 0 citations