Transformer With Regularized Dual Modal Meta Metric Learning for Attribute-Image Person Re-Identification

Attribute-image person re-identification (AIPR) is a meaningful and challenging task to retrieve images based on attribute descriptions. In this paper, we propose a regularized dual modal meta metric learning ((RDML)-L-3) method for AIPR, which employs meta-learning training methods to enhance the transformer's capacity to acquire latent knowledge. During training, data are initially divided into a single-modal support set with images and a dual-modal query set containing both attributes and images. The (RDML)-L-3 method introduces an attribute-image transformer (AIT) as a novel feature extraction backbone, extending the visual transformer concept. Utilizing the concept of hard sample mining, the method designs attribute-image cross-modal meta metrics and image-image intra-modal meta metrics. The triple loss function based on meta-metrics is then applied to converge the same category samples and diverge different categories, thereby enhancing cross-modal and intramodal discrimination abilities. Finally, a regularization term is used to aggregate samples of different modalities in the query set to prevent overfitting, ensuring that (RDML)-L-3 maintains the model's generalization ability while aligning the two modalities and identifying unseen classes. Experimental results on the PETA and Market-1501 attribute datasets demonstrate the superiority of the (RDML)-L-3 method, achieving mean average precision (mAP) scores of 36.7% on the Market-1501 Attributes dataset and 60.6% on the PETA dataset.