Erratum: Integration graph attention network and multi-centre constrained loss for cross-modality person re-identification

IF 1.5 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IET Computer Vision Pub Date : 2023-06-01 DOI:10.1049/cvi2.12210

引用次数: 0

Abstract

The authors wish to bring to the readers' attention the following errors in the article by He, D., et al.: Integration graph attention network and multi-centre constrained loss for cross-modality person re-identification [1].

In Funding Information section the funding number for National Natural Science Foundation of China is incorrectly mentioned as 2022KYCX032Z. It should be 62171321.

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勘误表：跨模态人再识别的集成图注意力网络和多中心约束损失

作者希望提请读者注意何等文章中的以下错误：跨模态人再识别的集成图注意力网络和多中心约束损失[1]。在资助信息部分，国家自然科学基金的资助编号被错误地提到为2022KYCX032Z。应该是62171321。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IET Computer Vision 工程技术-工程：电子与电气

CiteScore

3.30

自引率

11.80%

发文量

审稿时长

3.4 months

期刊介绍： IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in computer vision. IET Computer Vision welcomes submissions on the following topics: Biologically and perceptually motivated approaches to low level vision (feature detection, etc.); Perceptual grouping and organisation Representation, analysis and matching of 2D and 3D shape Shape-from-X Object recognition Image understanding Learning with visual inputs Motion analysis and object tracking Multiview scene analysis Cognitive approaches in low, mid and high level vision Control in visual systems Colour, reflectance and light Statistical and probabilistic models Face and gesture Surveillance Biometrics and security Robotics Vehicle guidance Automatic model aquisition Medical image analysis and understanding Aerial scene analysis and remote sensing Deep learning models in computer vision Both methodological and applications orientated papers are welcome. Manuscripts submitted are expected to include a detailed and analytical review of the literature and state-of-the-art exposition of the original proposed research and its methodology, its thorough experimental evaluation, and last but not least, comparative evaluation against relevant and state-of-the-art methods. Submissions not abiding by these minimum requirements may be returned to authors without being sent to review. Special Issues Current Call for Papers: Computer Vision for Smart Cameras and Camera Networks - https://digital-library.theiet.org/files/IET_CVI_SC.pdf Computer Vision for the Creative Industries - https://digital-library.theiet.org/files/IET_CVI_CVCI.pdf