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Existing pipelines of semantic correspondence commonly include extractinghigh-level semantic features for the invariance against intra-class variationsand background clutters. This architecture, however, inevitably results in alow-resolution matching field that additionally requires an ad-hocinterpolation process as a post-processing for converting it into ahigh-resolution one, certainly limiting the overall performance of matchingresults. To overcome this, inspired by recent success of implicit neuralrepresentation, we present a novel method for semantic correspondence, calledNeural Matching Field (NeMF). However, complicacy and high-dimensionality of a4D matching field are the major hindrances, which we propose a cost embeddingnetwork to process a coarse cost volume to use as a guidance for establishinghigh-precision matching field through the following fully-connected network.Nevertheless, learning a high-dimensional matching field remains challengingmainly due to computational complexity, since a naive exhaustive inferencewould require querying from all pixels in the 4D space to infer pixel-wisecorrespondences. To overcome this, we propose adequate training and inferenceprocedures, which in the training phase, we randomly sample matching candidatesand in the inference phase, we iteratively performs PatchMatch-based inferenceand coordinate optimization at test time. With these combined, competitiveresults are attained on several standard benchmarks for semanticcorrespondence. Code and pre-trained weights are available athttps://ku-cvlab.github.io/NeMF/.

Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence