Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences

  title={Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences},
  author={Prune Truong and Martin Danelljan and Fisher Yu and Luc Van Gool},
We propose Probabilistic Warp Consistency, a weaklysupervised learning objective for semantic matching. Our approach directly supervises the dense matching scores predicted by the network, encoded as a conditional probability distribution. We first construct an image triplet by applying a known warp to one of the images in a pair depicting different instances of the same object class. Our probabilistic learning objectives are then derived using the constraints arising from the resulting image… 


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