T<sup>3</sup>FRNet: A Cloth-Changing Person Re-identification via Texture-aware Transformer Tuning Fine-grained Reconstruction Method

ZHUANG Jianjun; WANG Nan

doi:10.11999/JEIT250476

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ZHUANG Jianjun, WANG Nan. T3FRNet: A Cloth-Changing Person Re-identification via Texture-aware Transformer Tuning Fine-grained Reconstruction Method[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250476

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ZHUANG Jianjun, WANG Nan. T³FRNet: A Cloth-Changing Person Re-identification via Texture-aware Transformer Tuning Fine-grained Reconstruction Method[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250476

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T³FRNet: A Cloth-Changing Person Re-identification via Texture-aware Transformer Tuning Fine-grained Reconstruction Method

doi: 10.11999/JEIT250476 cstr: 32379.14.JEIT250476

ZHUANG Jianjun^,,
WANG Nan

School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (62272234), Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX25_0503)

Received Date: 2025-05-27
Accepted Date: 2025-11-03
Rev Recd Date: 2025-11-03

Available Online: 2025-11-12

Abstract

Abstract

Objective Compared with conventional person re-identification, Cloth-Changing Person Re-Identification (CC Re-ID) requires moving beyond reliance on the temporal stability of appearance features and instead demands models with stronger robustness and generalization to meet real-world application requirements. Existing deep feature representation methods leverage salient regions or attribute information to obtain discriminative features and mitigate the effect of clothing variations; however, their performance often degrades under changing environments. To address the challenges of effective feature extraction and limited training samples in CC Re-ID tasks, a Texture-Aware Transformer Tuning Fine-Grained Reconstruction Network (T³FRNet) is proposed. The method aims to exploit fine-grained information in person images, enhance the robustness of feature learning, and reduce the adverse effect of clothing changes on recognition performance, thereby alleviating performance bottlenecks under scene variations. Methods To compensate for the limitations of local receptive fields, a Transformer-based attention mechanism is integrated into a ResNet50 backbone, forming a hybrid architecture referred to as ResFormer50. This design enables spatial relationship modeling on top of local features and improves perceptual capacity for feature extraction while maintaining a balance between efficiency and performance. A fine-grained Texture-Aware (TA) module concatenates processed texture features with deep semantic features, improving recognition capability under clothing variations. An Adaptive Hybrid Pooling (AHP) module performs channel-wise autonomous aggregation, allowing deeper mining of feature representations and balancing global representation consistency with robustness to clothing changes. An Adaptive Fine-Grained Reconstruction (AFR) strategy introduces adversarial perturbations and selective reconstruction at the fine-grained level. Without explicit supervision, this strategy enhances robustness and generalization against clothing changes and local detail perturbations. In addition, a Joint Perception Loss (JP-Loss) is constructed by integrating fine-grained identity robustness loss, texture feature loss, identity classification loss, and triplet loss. This composite loss jointly supervises the learning of robust fine-grained identity features under cloth-changing conditions. Results and Discussions Extensive evaluations are conducted on LTCC, PRCC, Celeb-reID, and the large-scale DeepChange dataset (Table 1). Under cloth-changing scenarios, the proposed method achieves Rank-1/mAP scores of 45.6%/19.8% on LTCC, 70.6%/69.1% on PRCC (Table 2), 64.6%/18.4% on Celeb-reID (Table 3), and 58.0%/20.8% on DeepChange (Table 4), outperforming existing state-of-the-art approaches. The TA module effectively captures latent local texture details and, when combined with the AFR strategy, enables fine-grained adversarial perturbation and selective reconstruction. This improves fine-grained feature representation and allows the method to achieve 96.2% Rank-1 and 89.3% mAP on the clothing-consistent Market-1501 dataset (Table 5). The JP-Loss further supports the TA module and AFR strategy by enabling fine-grained adaptive regulation and clustering of texture-sensitive identity features (Table 6). When the Transformer-based attention mechanism is inserted after stage 2 of ResNet50, improved local structural perception and global context modeling are obtained with only a slight increase in computational overhead (Table 7). Setting the $ \beta $ parameter to 0.5 (Fig. 5) enables effective balancing of global texture consistency and local fine-grained discriminability. Visualization results on PRCC (Fig. 6) and top-10 retrieval comparisons (Fig. 7) provide intuitive evidence of improved stability and accuracy in cloth-changing scenarios. Conclusions A CC Re-ID method based on T³FRNet is proposed, consisting of the ResFormer50 backbone, TA module, AHP module, AFR strategy, and JP-Loss. Experimental results on four cloth-changing benchmarks and one clothing-consistent dataset confirm the effectiveness of the proposed approach. Under long-term scenarios, Rank-1/mAP improvements of 16.8%/8.3% on LTCC and 30.4%/32.9% on PRCC are achieved. The ResFormer50 backbone supports spatial relationship modeling over local fine-grained features, while the TA module and AFR strategy enhance feature expressiveness. The AHP module balances sensitivity to local textures and stability of global features, and JP-Loss strengthens adaptive regulation of fine-grained representations. Future work will focus on simplifying the architecture to reduce computational complexity and latency while maintaining high recognition accuracy.
- Person re-identification,
- Cloth-changing,
- Texture-aware,
- Transformer,
- Fine-grained reconstruction

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References(25)

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