Person Search by Text Attribute Query As Zero-Shot Learning

Qi Dong, Shaogang Gong, Xiatian Zhu; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 3652-3661

Abstract


Existing person search methods predominantly assume the availability of at least one-shot imagery sample of the queried person. This assumption is limited in circumstances where only a brief textual (or verbal) description of the target person is available. In this work, we present a deep learning method for attribute text description based person search without any query imagery. Whilst conventional cross-modality matching methods, such as global visual-textual embedding based zero-shot learning and local individual attribute recognition, are functionally applicable, they are limited by several assumptions invalid to person search in deployment scale, data quality, and/or category name semantics. We overcome these issues by formulating an Attribute-Image Hierarchical Matching (AIHM) model. It is able to more reliably match text attribute descriptions with noisy surveillance person images by jointly learning global category-level and local attribute-level textual-visual embedding as well as matching. Extensive evaluations demonstrate the superiority of our AIHM model over a wide variety of state-of-the-art methods on three publicly available attribute labelled surveillance person search benchmarks: Market-1501, DukeMTMC, and PA100K.

Related Material


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[bibtex]
@InProceedings{Dong_2019_ICCV,
author = {Dong, Qi and Gong, Shaogang and Zhu, Xiatian},
title = {Person Search by Text Attribute Query As Zero-Shot Learning},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}