Learning to Purification for Unsupervised Person Re-Identification

Meta Pairwise Relationship Distillation for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) is challenging due to the lack of ground-truth labels. Most existing methods rely on pseudo labels estimated via iterative clustering and thus are highly susceptible to performance penalties incurred by the inaccurate estimated number of clusters. Alternatively, we utilize the sample pairs with pairwise pseudo labels to guide the feature learning to avoid the dilemma of determining cluster numbers. In this article, we propose a meta pairwise relationship distillation (MPRD) method that incorporates a graph convolutional network (GCN) to provide high-fidelity pairwise relationships to supervise the model training. A small amount of metadata with very-confidence pairwise relationships and the unlabeled pairs with the provided pseudo pairwise relationships participate in the GCN training. Besides, we introduce a hard sample deduction (HSD) module to timely mine the sample pairs with error-prone pairwise pseudo labels to mitigate the misled optimization by noisy labels. Furthermore, since the features of each positive pair represent the same person, we design a positive pair alignment (PPA) module to reduce the redundant information in each feature, which is achieved by minimizing the difference between each positive pair's feature distributions. Extensive experiments on the Market-1501, DukeMTMC-reID, and MSMT17 datasets show that our method outperforms the state-of-the-art unsupervised methods.

Unsupervised Learning of Intrinsic Semantics with Diffusion Model for Person Re-Identification

Unsupervised person re-identification (Re-ID) aims to learn semantic representations for person retrieval without using identity labels. Most existing methods generate fine-grained patch features to reduce noise in global feature clustering. However, these methods often compromise the discriminative semantic structure and overlook the semantic consistency between the patch and global features. To address these problems, we propose a Person Intrinsic Semantic Learning (PISL) framework with diffusion model for unsupervised person Re-ID. First, we design the Spatial Diffusion Model (SDM), which performs a denoising diffusion process from noisy spatial transformer parameters to semantic parameters, enabling the sampling of patches with intrinsic semantic structure. Second, we propose the Semantic Controlled Diffusion (SCD) loss to guide the denoising direction of the diffusion model, facilitating the generation of semantic patches. Third, we propose the Patch Semantic Consistency (PSC) loss to capture semantic consistency between the patch and global features, refining the pseudo-labels of global features. Comprehensive experiments on three challenging datasets show that our method surpasses current unsupervised Re-ID methods. The source code will be publicly available at https://github.com/taoxuefong/Diffusion-reid.

NTCE-KD: Non-Target-Class-Enhanced Knowledge Distillation

Most logit-based knowledge distillation methods transfer soft labels from the teacher model to the student model via Kullback-Leibler divergence based on softmax, an exponential normalization function. However, this exponential nature of softmax tends to prioritize the largest class (target class) while neglecting smaller ones (non-target classes), leading to an oversight of the non-target classes's significance. To address this issue, we propose Non-Target-Class-Enhanced Knowledge Distillation (NTCE-KD) to amplify the role of non-target classes both in terms of magnitude and diversity. Specifically, we present a magnitude-enhanced Kullback-Leibler (MKL) divergence multi-shrinking the target class to enhance the impact of non-target classes in terms of magnitude. Additionally, to enrich the diversity of non-target classes, we introduce a diversity-based data augmentation strategy (DDA), further enhancing overall performance. Extensive experimental results on the CIFAR-100 and ImageNet-1k datasets demonstrate that non-target classes are of great significance and that our method achieves state-of-the-art performance across a wide range of teacher-student pairs.

Robust Fine-Grained Visual Recognition With Neighbor-Attention Label Correction

Existing deep learning methods for fine-grained visual recognition often rely on large-scale, well-annotated training data. Obtaining fine-grained annotations in the wild typically requires concentration and expertise, such as fine category annotation for species recognition, instance annotation for person re-identification (re-id) and dense annotation for segmentation, which inevitably leads to label noise. This paper aims to tackle label noise in deep model training for fine-grained visual recognition. We propose a Neighbor-Attention Label Correction (NALC) model to correct labels during the training stage. NALC samples a training batch and a validation batch from the training set. It hence leverages a meta-learning framework to correct labels in the training batch based on the validation batch. To enhance the optimization efficiency, we introduce a novel nested optimization algorithm for the meta-learning framework. The proposed training procedure consistently improves label accuracy in the training batch, consequently enhancing the learned image representation. Experimental results demonstrate that our method significantly increases label accuracy from 70% to over 98% and outperforms recent approaches by up to 13.4% in mean Average Precision (mAP) on various fine-grained image retrieval (FGIR) tasks, including instance retrieval on CUB200 and person re-id on Market1501. We also demonstrate the efficacy of NALC on noisy semantic segmentation datasets generated from Cityscapes, where it achieves a significant 7.8% improvement in mIOU score. NALC also exhibits robustness to different types of noise, including simulated noise such as Asymmetric, Pair-Flip, and Pattern noise, as well as practical noisy labels generated by tracklets and clustering.