Lee et al., 2019 - Google Patents

Sfnet: Learning object-aware semantic correspondence

Lee et al., 2019

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Document ID
12786252395604462445
Author
Lee J
Kim D
Ponce J
Ham B
Publication year
Publication venue
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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Snippet

We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We propose to use images annotated with binary foreground masks and subjected to synthetic …
Continue reading at openaccess.thecvf.com (PDF) (other versions)

Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G06K9/6203—Shifting or otherwise transforming the patterns to accommodate for positional errors
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    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
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