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Establishing dense correspondences across semantically similar images is oneof the challenging tasks due to the significant intra-class variations andbackground clutters. To solve these problems, numerous methods have beenproposed, focused on learning feature extractor or cost aggregationindependently, which yields sub-optimal performance. In this paper, we proposea novel framework for jointly learning feature extraction and cost aggregationfor semantic correspondence. By exploiting the pseudo labels from each module,the networks consisting of feature extraction and cost aggregation modules aresimultaneously learned in a boosting fashion. Moreover, to ignore unreliablepseudo labels, we present a confidence-aware contrastive loss function forlearning the networks in a weakly-supervised manner. We demonstrate ourcompetitive results on standard benchmarks for semantic correspondence.

Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence