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Finding semantic correspondences is a challenging problem. With thebreakthrough of CNNs stronger features are available for tasks likeclassification but not specifically for the requirements of semantic matching.In the following we present a weakly supervised metric learning approach whichgenerates stronger features by encoding far more context than previous methods.First, we generate more suitable training data using a geometrically informedcorrespondence mining method which is less prone to spurious matches andrequires only image category labels as supervision. Second, we introduce a newconvolutional layer which is a learned mixture of differently stridedconvolutions and allows the network to encode implicitly more context whilepreserving matching accuracy at the same time. The strong geometric encoding onthe feature side enables us to learn a semantic flow network, which generatesmore natural deformations than parametric transformation based models and isable to jointly predict foreground regions at the same time. Our semantic flownetwork outperforms current state-of-the-art on several semantic matchingbenchmarks and the learned features show astonishing performance regardingsimple nearest neighbor matching.

Multi-Scale Convolutions for Learning Context Aware Feature Representations