Joint Attention Mechanism for Person Re-Identification | IEEE Journals & Magazine | IEEE Xplore

Joint Attention Mechanism for Person Re-Identification


An overview of our architecture JA-ReID, which consists of a global feature branch to extract the global feature, a hard region-level attention branch to conduct uniform ...

Abstract:

Although person re-identification (ReID) has drawn increasing research attention due to its potential to address the problem of analysis and processing of massive monitor...Show More

Abstract:

Although person re-identification (ReID) has drawn increasing research attention due to its potential to address the problem of analysis and processing of massive monitoring data, it is very challenging to learn discriminative information when the people in the images are occluded, in large pose variations or from different perspectives. To address this problem, we propose a novel joint attention person ReID (JA-ReID) architecture. The idea is to learn two complementary feature representations by combining a soft pixel-level attention mechanism and a hard region-level attention mechanism. The soft pixel-level attention mechanism learns a discriminative embedding for the fine-grained information by exploring the salient parts in the feature maps. The hard region-level attention mechanism conducts uniform partitions on the convolutional feature maps for learning local features. We have achieved competitive results in three popular benchmarks, including Market1501, DukeMTMC-reID, and CUHK03. The experimental results verify the adaptability of the joint attention mechanism to non-rigid deformation of the human body, which can effectively improve the accuracy of ReID.
An overview of our architecture JA-ReID, which consists of a global feature branch to extract the global feature, a hard region-level attention branch to conduct uniform ...
Published in: IEEE Access ( Volume: 7)
Page(s): 90497 - 90506
Date of Publication: 08 July 2019
Electronic ISSN: 2169-3536

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