Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

Abstract Contrastive Language-Image Pre-training (CLIP) has made a remarkable
breakthrough in open-vocabulary zero-shot image recognition. Many recent studies …

In this paper, we study the semi-supervised semantic segmentation problem via exploring
both labeled data and extra unlabeled data. We propose a novel consistency regularization …

Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage
unlabeled data. In this work, we first construct a strong baseline of self-training (namely ST) …

Consistency learning using input image, feature, or network perturbations has shown
remarkable results in semi-supervised semantic segmentation, but this approach can be …

We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-
quality semantically segmented images requiring minimal human effort. Current deep …

Semantic segmentation labels are expensive and time consuming to acquire. To improve
label efficiency of semantic segmentation models, we revisit denoising autoencoders and …

In this paper, we present a novel cross-consistency based semi-supervised approach for
semantic segmentation. Consistency training has proven to be a powerful semi-supervised …

We present a novel semi-supervised semantic segmentation method which jointly achieves
two desiderata of segmentation model regularities: the label-space consistency property …