Text-based person search via attribute-aided matching

Aggarwal, S and Babu, RV and Chakraborty, A (2020) Text-based person search via attribute-aided matching. In: IEEE/CVF Winter Conference on Applications of Computer Vision, 1-5 March 2020, Snowmass Village, United States, pp. 2606-2614.

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Abstract

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 well-explored 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 text-based 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. © 2020 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	Copyright of this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Benchmarking; Computer vision; Semantics, Benchmark datasets; Best match; Class information; Image texts; Improving performance; State of the art; Text corpora; Text query, Image enhancement
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	08 Apr 2021 06:17
Last Modified:	08 Apr 2021 06:17
URI:	http://eprints.iisc.ac.in/id/eprint/65619

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