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 to conventional person re-identification, Cloth-Changing person Re-Identification (CC Re-ID) requires moving beyond the reliance on the stability of a person’s appearance features over time, instead demanding models with greater robustness and generalization capabilities to address real-world application scenarios. Existing deep feature representation methods can leverage salient regions or attribute information to obtain discriminative features and mitigate the impact of clothing variations, yet their performance often degrades under changing environments. To address the challenges of effective feature extraction and limited training samples in CC Re-ID tasks, this paper proposes a novel Texture-aware Transformer Tuning Fine-grained Reconstruction Network (T³FRNet), which aims to fully exploit fine-grained information within person images, enhance the robustness of feature learning, and reduce the adverse impact of clothing changes on model performance, ultimately overcoming performance bottlenecks under scene variations. Methods To better compensate for the limitations of local receptive fields, T³FRNet incorporates a Transformer-based attention mechanism into the ResNet50 backbone, constructing a hybrid architecture named ResFormer50. This design facilitates spatial relational modeling on top of local features, enhancing the model’s perceptual capacity for feature extraction while maintaining a balance between efficiency and performance. The fine-grained Texture-Aware (TA) module concatenates processed texture features with deep semantic features, thereby improving the model’s recognition capability under clothing variations. Meanwhile, the Adaptive Hybrid Pooling (AHP) module performs channel-wise autonomous aggregation, enabling deeper and more refined mining of feature representations. This contributes to achieving a balance between global representation consistency and robustness to clothing changes. A novel Adaptive Fine-grained Reconstruction (AFR) strategy introduces adversarial perturbations and selective reconstruction at a fine-grained level. Without relying on explicit supervision, the AFR strategy significantly enhances the model’s robustness and generalization against clothing changes and local detail perturbations, thereby improving recognition accuracy in real-world scenarios. Finally, a Joint Perception Loss (JP-Loss) is designed by integrating fine-grained identity robustness loss, texture feature loss, the widely used identity classification loss, and triplet loss. This composite loss jointly supervises the model to learn robust fine-grained identity features, ultimately boosting its performance under challenging cloth-changing conditions. Results and Discussions To validate the effectiveness of the proposed model, extensive evaluations are conducted on two widely used CC Re-ID benchmarks, LTCC, PRCC and Celeb-reID, as well as a large-scale dataset, DeepChange (Table 1). Under cloth-changing scenarios, the model 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 extracts latent local texture details within person images and, in conjunction with the AFR strategy, performs fine-grained adversarial perturbations and selective reconstruction. This enhances fine-grained feature representations, enabling the proposed method to also achieve 96.2% Rank-1 and 89.3% mAP on the clothing-consistent Market-1501 dataset (Table 5). The introduction of 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). Furthermore, when Transformer-based attention mechanism is integrated after stage2 of the ResNet50, the model achieves improved local structural perception and global context modeling with only a slight increase in computational overhead, thereby enhancing overall performance (Table 7). Additionally, setting the $ \beta $ parameter to 0.5 (Fig.5) enables the JP-Loss to effectively balance global texture consistency and local fine-grained discriminability, thereby enhancing the overall robustness and accuracy of CC Re-ID. Finally, visualization experiments based on the PRCC dataset (Fig.6) offer intuitive evidence of the model’s superior feature extraction capability and highlight the significance of the Transformer-based attention mechanism. The top 10 ranking retrieval results of the baseline model and T³FRNet in the clothing changing scenario (Fig.7) intuitively demonstrate that T³FRNet has better stability and accuracy. Conclusions This paper proposes a CC Re-ID method based on T³FRNet, composed of the ResFormer50 backbone, TA module, AHP module, AFR strategy, and JP-Loss. Extensive experiments conducted on four publicly available cloth-changing benchmarks and one clothing-consistent dataset demonstrate the effectiveness and superiority of the proposed approach. Under long-term scenarios, Rank-1/mAP on the LTCC and PRCC datasets achieve significant improvements of 16.8%/8.3% and 30.4%/32.9% respectively. The ResFormer50 backbone facilitates spatial relationship modeling on top of local fine-grained features, while the TA module and AFR strategy enhance the expressiveness of fine-grained representations. The AHP module effectively balances the model’s sensitivity to local textures with the stability of global features, thereby ensuring strong feature representation alongside robustness. JP-Loss assists the model in constraining fine-grained feature representations and performing adaptive regulation, thereby enhancing its generalization capability in diverse and challenging cloth-changing scenarios. Future work will focus on simplifying the model architecture to reduce computational complexity and latency, aiming to strike a better balance between high recognition accuracy and deployment efficiency.
- Person re-identification,
- Cloth-changing,
- Texture-aware,
- Transformer,
- Fine-grained reconstruction

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References(25)

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