CAIBC: Capturing All-round Information Beyond Color For Text-based Person Retrieval

Abstract

Given a natural language description, text-based person retrieval aims to identify images of a target person from a large-scale person image database. Existing methods generally face a \textbf\{color over-reliance problem\}, which means that the models rely heavily on color information when matching cross-modal data. Indeed, color information is an important decision-making accordance for retrieval, but the over-reliance on color would distract the model from other key clues (e.g. texture information, structural information, etc.), and thereby lead to a sub-optimal retrieval performance. To solve this problem, in this paper, we propose to \textbf\{C\}apture \textbf\{A\}ll-round \textbf\{I\}nformation \textbf\{B\}eyond \textbf\{C\}olor (\textbf\{CAIBC\}) via a jointly optimized multi-branch architecture for text-based person retrieval. CAIBC contains three branches including an RGB branch, a grayscale (GRS) branch and a color (CLR) branch. Besides, with the aim of making full use of all

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• arxiv keywang2022caibc

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