Learning Contrastive Representation for Semantic Correspondence

TL;DR

This paper introduces a multi-level contrastive learning method for semantic correspondence that does not depend on ImageNet pretraining, effectively handling variations and reducing labeling efforts.

Contribution

It proposes a novel self-supervised contrastive learning framework for pixel-level semantic matching without relying on pretrained models.

Findings

Outperforms state-of-the-art on PF-PASCAL, PF-WILLOW, and SPair-71k datasets.

Effectively handles large appearance, scale, and pose variations.

Reduces dependence on labor-intensive pixel-level annotations.

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 approaches focus on designing various matching approaches with 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. The source code and trained models will be made available to the public.