3 accepted papers
Semantic correspondence (SC) aims to establish semantically meaningful matches across different instances of an object category. We illustrate how recent supervised SC methods remain limited in their ability to generalize beyond sparsely annotated training keypoints, effectively acting as keypoint d…
Recent self-supervised models produce visual features that are not only effective at encoding image-level but also pixel-level semantics. They have been reported to obtain impressive results for dense visual semantic correspondence estimation even outperforming fully-supervised methods. Nevertheless…
Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are expensive to obtain on a large scale. In this paper, we address the problem of unsupervised viewpoint estimation. We fo…