Author(s): Ning, X (Ning, Xin); Gong, K (Gong, Ke); Li, WJ (Li, Weijun); Zhang, LP (Zhang, Liping)

Source: NEUROCOMPUTING Volume: 453 Pages: 801-811 DOI: 10.1016/j.neucom.2020.05.106 Published: SEP 17 2021

Abstract: Attention mechanisms can extract salient features in images, which has been proven to be effective for person re-identification. However, focusing on the saliency of an image is not enough. On the one hand, the salient features extracted from the model are not necessarily the features needed, e.g., a similar background may also be mistaken as salient features; on the other hand, various salient features are often more conducive to improving the performance of the model. Based on this, in this paper, a model that has joint weak saliency and attention aware is proposed, which can obtain more complete global features by weakening saliency features. The model then obtains diversified saliency features via attention diversity to improve the performance of the model. Experiments on commonly used datasets prove the effectiveness of the proposed method.

(c) 2020 Elsevier B.V. All rights reserved.

Accession Number: WOS:000663418300001

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Ning, Xin         M-9479-2018         0000-0001-7897-1673

ISSN: 0925-2312

eISSN: 1872-8286

Full Text: https://www.sciencedirect.com/science/article/pii/S0925231220313606?via%3Dihub