Unsupervised Person Re-Identification with Pseudo Label Regularization Loss
Unsupervised person re-identification aims to match query pedestrian images with images in the gallery without the need for identity labels.Currently,mainstream unsupervised person re-identification methods typically utilize clustering algorithms to generate pseudo-labels,which are subsequently exploited to train deep neural networks.However,due to the model's inferior representation ability at early stages and the limitations of the clustering algorithms,a vast of noise is inevitably introduced into the pseudo-labels,which seriously misleads the training process and impedes the model performance.In this paper,we propose a novel pseudo-label regularization loss(PLRL)to remedy the detrimental effect of pseudo-label noises.Concretely,firstly,this paper proposes a clustering-guided attention mechanism(CGA)to estimate the confidence of pseudo-labels based on the semantic relevance between pseudo-labels and clustering centers.The CGA score is able to identify noisy labels and assign more weight to correct labels,which effectively reduces the influence of pseudo-la-bel noise in the overall loss function.Meanwhile,for the sake of fully utilizing the discriminative power of pseudo-labels,this paper performs soft sample mining using pseudo-labels,which constructs positive and negative sample pairs in mini-batches and calculates a continuous weight score for each pair.By incorporating the confidence of pseudo-labels and the similarity of samples into the contrastive loss,the newly designed pseudo-label regularization loss can effectively alleviate the influence of pseudo-label noise in the training process,thereby improving the accuracy and robustness of the model.Ex-periments and ablation studies on multiple public datasets demonstrate its effectiveness and superiority,with the mAP on Market1501,DukeMTMC-reID,and MSMT17 datasets reaching 85.9%,75.1%,and 29.3%,respectively.
person re-identificationunsupervised learningpseudo label noisecontrastive learningclustering re-finement
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