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Current best local descriptors are learned on a large dataset of matching andnon-matching keypoint pairs. However, data of this kind is not always availablesince detailed keypoint correspondences can be hard to establish. On the otherhand, we can often obtain labels for pairs of keypoint bags. For example,keypoint bags extracted from two images of the same object under differentviews form a matching pair, and keypoint bags extracted from images ofdifferent objects form a non-matching pair. On average, matching pairs shouldcontain more corresponding keypoints than non-matching pairs. We describe anend-to-end differentiable architecture that enables the learning of localkeypoint descriptors from such weakly-labeled data. Additionally, we discusshow to improve the method by incorporating the procedure of mining hardnegatives. We also show how can our approach be used to learn convolutionalfeatures from unlabeled video signals and 3D models.  Our implementation is available at https://github.com/nenadmarkus/wlrn

ieee transactions on image processing

Learning Local Descriptors by Optimizing the Keypoint-Correspondence  Criterion: Applications to Face Matching, Learning from Unlabeled Videos and  3D-Shape Retrieval