1. [Publications](/publications) 2. Learning Contrastive Representation for Semantic Correspondence # Learning Contrastive Representation for Semantic Correspondence ![](/sites/default/files/styles/wide/public/publications/teaser_th_iccv_0.png?itok=uq6rrg7P) **ABSTRACT** Dense correspondence across semantically related images has been extensively studied, but still faces two challenges: 1) large variations in appearance, scale and pose exist even for objects from the same category, and 2) labeling pixel-level dense correspondences is labor intensive and infeasible to scale. Most existing methods focus on designing various matching modules using fully-supervised ImageNet pretrained networks. On the other hand, while a variety of self-supervised approaches are proposed to explicitly measure image-level similarities, correspondence matching the pixel level remains under-explored. In this work, we propose a multi-level contrastive learning approach for semantic matching, which does not rely on any ImageNet pretrained model. We show that image-level contrastive learning is a key component to encourage the convolutional features to find correspondence between similar objects, while the performance can be further enhanced by regularizing cross-instance cycle-consistency at intermediate feature levels. Experimental results on the PF-PASCAL, PF-WILLOW, and SPair-71k benchmark datasets demonstrate that our method performs favorably against the state-of-the-art approaches. ## Authors Taihong Xiao (UC Merced) [Sifei Liu](/person/sifei-liu) [Shalini De Mello](/person/shalini-de-mello) [Zhiding Yu](/person/zhiding-yu) [Jan Kautz](/person/jan-kautz) Ming-Hsuan Yang (UC Merced) ## Publication Date Tuesday, March 22, 2022 ## Published in [International Journal of Computer Vision (IJCV) 2022](https://link.springer.com/article/10.1007/s11263-022-01602-y) ## Research Area [Computer Vision](/research-area/computer-vision) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .