CGR-Net: Consistency Guided ResFormer for Two-View Correspondence Learning

IF 8.3 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-06 DOI:10.1109/tcsvt.2024.3439348

Changcai Yang, Xiaojie Li, Jiayi Ma, Fengyuan Zhuang, Lifang Wei, Riqing Chen, Guodong Chen

引用次数: 0

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CGR-Net：用于双视图对应学习的一致性指导重构器

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

IEEE Transactions on Circuits and Systems for Video Technology 工程技术-工程：电子与电气

CiteScore

13.80

自引率

27.40%

发文量

660

审稿时长

5 months

期刊介绍： The IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) is dedicated to covering all aspects of video technologies from a circuits and systems perspective. We encourage submissions of general, theoretical, and application-oriented papers related to image and video acquisition, representation, presentation, and display. Additionally, we welcome contributions in areas such as processing, filtering, and transforms; analysis and synthesis; learning and understanding; compression, transmission, communication, and networking; as well as storage, retrieval, indexing, and search. Furthermore, papers focusing on hardware and software design and implementation are highly valued. Join us in advancing the field of video technology through innovative research and insights.