Semi-Global Context Network for Semantic Correspondence
Abstract
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 semantic context to enable robust semantic correspondence estimation against intra-class variation and repetitive patterns. We introduce a global context...
Paper Details
Title
Semi-Global Context Network for Semantic Correspondence
Published Date
Dec 1, 2021
Journal
Volume
9
Pages
2496 - 2507
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