AutoScaler: Scale-Attention Networks for Visual Correspondence

british machine vision conference, 2017.

Cited by: 12|Bibtex|Views160|DOI:https://doi.org/10.5244/c.31.185
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Finding visual correspondence between local features is key to many computer vision problems. While defining features with larger contextual scales usually implies greater discriminativeness, it could also lead to less spatial accuracy of the features. We propose AutoScaler, a scale-attention network to explicitly optimize this trade-off ...More

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