收录:
摘要:
This article presents a novel person reidentification model, named multihead self-attention network (MHSA-Net), to prune unimportant information and capture key local information from person images. MHSA-Net contains two main novel components: multihead self-attention branch (MHSAB) and attention competition mechanism (ACM). The MHSAB adaptively captures key local person information and then produces effective diversity embeddings of an image for the person matching. The ACM further helps filter out attention noise and nonkey information. Through extensive ablation studies, we verified that the MHSAB and ACM both contribute to the performance improvement of the MHSA-Net. Our MHSA-Net achieves competitive performance in the standard and occluded person Re-ID tasks.
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通讯作者信息:
来源 :
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN: 2162-237X
年份: 2022
期: 11
卷: 34
页码: 8210-8224
1 0 . 4
JCR@2022
1 0 . 4 0 0
JCR@2022
ESI学科: COMPUTER SCIENCE;
ESI高被引阀值:46
JCR分区:1
中科院分区:1
归属院系: