MACA: Memory-aided Coarse-to-fine Alignment for Text-based Person Search

Text-based person search (TBPS) aims to search for the target person in the full image through textual descriptions. The key to addressing this task is to effectively perform cross-modality alignment between text and images. In this paper, we propose a novel TBPS framework, named Memory-Aided Coarse-to-fine Alignment (MACA), to learn an accurate and reliable alignment between the two modalities. Firstly, we introduce a proposal-based alignment module, which performs contrastive learning to accurately align the textual modality with different pedestrian proposals at a coarse-grained level. Secondly, for the fine-grained alignment, we propose an attributebased alignment module to mitigate unreliable features by aligning text-wise details with image-wise global features. Moreover, we introduce an intuitive memory bank strategy to supplement useful negative samples for more effective contrastive learning, improving the convergence and generalization ability of the model based on the learned discriminative features. Extensive experiments on CUHK-SYSU-TBPS and PRW-TBPS demonstrate the superiority of MACA over state-of-the-art approaches. The code is available at https://github.com/suliangxu/MACA.