AutoScaler: Scale-Attention Networks for Visual CorrespondenceEI

    Cited by: 5|Bibtex|21|

    BMVC, 2017.

    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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