SCNet: Learning Semantic Correspondence
This addresses the challenge of semantic correspondence for computer vision applications, representing an incremental improvement over previous methods.
The paper tackles the problem of establishing semantic correspondences between images of the same object or scene category by proposing SCNet, a convolutional neural network that incorporates geometric consistency, and it substantially outperforms recent deep learning and hand-crafted feature methods on standard benchmarks.
This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category. Previous approaches focus on either combining a spatial regularizer with hand-crafted features, or learning a correspondence model for appearance only. We propose instead a convolutional neural network architecture, called SCNet, for learning a geometrically plausible model for semantic correspondence. SCNet uses region proposals as matching primitives, and explicitly incorporates geometric consistency in its loss function. It is trained on image pairs obtained from the PASCAL VOC 2007 keypoint dataset, and a comparative evaluation on several standard benchmarks demonstrates that the proposed approach substantially outperforms both recent deep learning architectures and previous methods based on hand-crafted features.