• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Tan, Hongchen (Tan, Hongchen.) | Liu, Xiuping (Liu, Xiuping.) | Yin, Baocai (Yin, Baocai.) (学者:尹宝才) | Li, Xin (Li, Xin.)

收录:

EI Scopus SCIE

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.

关键词:

feature fusion occluded person re-identification (Re-ID) multihead self-attention Attention competition mechanism (ACM) Standards Probes Adaptation models Feature extraction Computational modeling Task analysis Tensors

作者机构:

  • [ 1 ] [Tan, Hongchen]Beijing Univ Technol, Artificial Intelligence Res Inst, Beijing 100124, Peoples R China
  • [ 2 ] [Yin, Baocai]Beijing Univ Technol, Artificial Intelligence Res Inst, Beijing 100124, Peoples R China
  • [ 3 ] [Liu, Xiuping]Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China
  • [ 4 ] [Li, Xin]Louisiana State Univ, Sch Elect Engn & Comp Sci, Baton Rouge, LA 70808 USA
  • [ 5 ] [Li, Xin]Louisiana State Univ, Ctr Computat & Technol, Baton Rouge, LA 70808 USA

通讯作者信息:

查看成果更多字段

相关关键词:

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

被引次数:

WoS核心集被引频次: 72

SCOPUS被引频次: 70

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 8

归属院系:

在线人数/总访问数:391/4274136
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司