Text-based Person Search via Attribute-aided Matching

Text-based person search aims to retrieve the pedestrian images that best match a given text query. Existing methods utilize class-id information to get discriminative and identity-preserving features. However, it is not wellexplored whether it is beneficial to explicitly ensure that the semantics of the data are retained. In the proposed work, we aim to create semantics-preserving embeddings through an additional task of attribute prediction. Since attribute annotation is typically unavailable in text-based person search, we first mine them from the text corpus. These attributes are then used as a means to bridge the modality gap between the image-text inputs, as well as to improve the representation learning. In summary, we propose an approach for textbased person search by learning an attribute-driven space along with a class-information driven space, and utilize both for obtaining the retrieval results. Our experiments on benchmark dataset, CUHK-PEDES, show that learning the attribute-space not only helps in improving performance, giving us state-of-the-art Rank-1 accuracy of 56.68%, but also yields humanly-interpretable features.