Semi-Global Context Network for Semantic Correspondence

Semi-Global Context Network for Semantic Correspondence

Estimating semantic correspondence between pairs of images can be challenging as a result of intra-class variation, background clutter, and repetitive patterns. This paper proposes a convolutional neural network (CNN) that attempts to learn rich semantic representations that contain the global seman...

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Bibliographic Details
Main Authors: Ho-Jun Lee, Hong Tae Choi, Sung Kyu Park, Ho-Hyun Park
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Context fusion
historical averaging
neighborhood consensus network
semantic correspondence
semi-global self-similarity
weakly supervised learning
Online Access:https://ieeexplore.ieee.org/document/9305227/

Internet

https://ieeexplore.ieee.org/document/9305227/

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