Dual-Color Granularity Alignment for Text-Based Person Search

Text-based Person Search (TBPS) aims to retrieve the person images based on the given text descriptions. Due to the heterogeneity between modalities and the fine granularity of the person, it is challenging to address the task. Existing methods often overlook granularity consistency across different color channels, which means there’s much potential to enhance retrieval performance. In this paper, we propose a Dual-Color Granularity Alignment (DCGA) method for Text-Based Person Search. DCGA harnesses both color and grayscale information to address issues of color reliance and granularity consistency. Moreover, by employing an improved CR Loss with grayscale information used as an additional weak supervision, DCGA addresses intra-class variance and dataset scarcity. Extensive experiments have demonstrated that our proposed DCGA method achieves state-of-the-art results on all three public datasets.