Abstract
Mask learning has emerged as a promising approach for Text-to-Image Person Search (TIPS), yet it faces two key challenges: (1) There tends to be semantic inconsistency between image regions and text phrases. (2) Current approaches primarily focus on masking text tokens to facilitate cross-modal alignment, overlooking the important role that text plays in guiding the learning of intricate details within images, which can lead to missed opportunities for capturing these details. In this paper, we are excited to introduce our proposed method called Image Region Semantic Enhancement and Symmetric Semantic Completion (RE-SSC). Specifically, our approach comprises two main components: Image Region Semantic Enhancement (IRSE) and Symmetric Semantic Completion (SSC). In IRSE, we initially apply superpixel segmentation to partition images into distinct patches based on low-level semantics. Subsequently, we leverage self-supervised consistency learning to transfer high-level semantic information from the global context of the image for local patches, enhancing local patch semantics. Within the SSC component, we have designed a symmetric semantic completion learning process that operates in both textual and visual directions, emphasizing global as well as local token learning to achieve effective alignment across modalities. We evaluated our method on three public datasets and are pleased to report competitive performance in addressing text-to-image pedestrian searches.