In semi-supervised medical image segmentation, there exist empirical mismatch problems
between labeled and unlabeled data distribution. The knowledge learned from the labeled …

Text-to-image diffusion models are now capable of generating images that are often
indistinguishable from real images. To generate such images, these models must …

Abstract Traditional Unsupervised Domain Adaptation (UDA) leverages the labeled source
domain to tackle the learning tasks on the unlabeled target domain. It can be more …

In this paper we address the challenge of matching semantically similar keypoints across
image pairs. Existing research indicates that the intermediate output of the UNet within the …

We study the problem of learning semantic image correspondences without manual
supervision. Previous works that tackled this problem rely on manually curated image pairs …

Graph matching (GM) has been a long-standing combinatorial problem due to its NP-hard
nature. Recently (deep) learning-based approaches have shown their superiority over the …

In this paper, we develop a weakly supervised learning algorithm to learn robust semantic
correspondences from large-scale datasets with only image-level labels. Following the spirit …

Existing datasets for semantic correspondence are often limited in terms of both the amount
of labeled data and diversity of labeled keypoints due to the tremendous cost of manual …

Recently, many semi-supervised object detection (SSOD) methods adopt teacher-student
framework and have achieved state-of-the-art results. However, the teacher network is tightly …

We propose SimSC, a remarkably simple framework, to address the problem of semantic
matching only based on the feature backbone. We discover that when fine-tuning ImageNet …