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JunYoung Gwak
Universal Correspondence Network
Christopher B. Choy, Manmohan Chandraker, JunYoung Gwak, Silvio Savarese
We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motions to intra-class shape or appearance variations. In contrast to previous CNN-based approaches that optimize a surrogate patch similarity objective, we use deep metric learning to directly learn a feature space that preserves either geometric or semantic similarity.