Text-Pedestrian Image Retrieval aims to use the text describing pedestrian appearance to retrieve the corresponding pedestrian image. This task involves not only modality discrepancy, but also the challenge of the textual diversity of pedestrians with the same identity. At present, although existing research progress has been made in text-pedestrian image retrieval, these methods do not comprehensively consider the above-mentioned problems. Considering these, this paper proposes a progressive feature mining and external knowledge-assisted feature purification method. Specifically, we use a progressive mining mode to enable the model to mine discriminative features from neglected information, thereby avoiding the loss of discriminative information and improving the expression ability of features. In addition, to further reduce the negative impact of modal discrepancy and text diversity on cross-modal matching, we propose to use other sample knowledge of the same modality, i.e., external knowledge to enhance identity-consistent features and weaken identity-inconsistent features. This process purifies features and alleviates the interference caused by textual diversity and negative sample correlation features of the same modal. Extensive experiments on three challenging datasets demonstrate the effectiveness and superiority of the proposed method, and the retrieval performance even surpasses that of the large-scale model-based method on large-scale datasets.

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