Progressive Feature Mining and External Knowledge-Assisted
  Text-Pedestrian Image Retrieval
Huafeng Li Shedan Yang Yafei Zhang Dapeng Tao Zhengtao Yu
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.