Deep learning algorithms have exhibited impressive performance across various computer
vision tasks; however, the challenge of overfitting persists, especially when dealing with …

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 …

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

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

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

This work presents a novel approach for semi-supervised semantic segmentation. The key
element of this approach is our contrastive learning module that enforces the segmentation …

The state of the art in semantic segmentation is steadily increasing in performance, resulting
in more precise and reliable segmentations in many different applications. However …

Semantic segmentation models based on convolutional neural networks have recently
displayed remarkable performance for a multitude of applications. However, these models …

While self-training has advanced semi-supervised semantic segmentation, it severely suffers
from the long-tailed class distribution on real-world semantic segmentation datasets that …

Collecting labeled data for the task of semantic segmentation is expensive and time-
consuming, as it requires dense pixel-level annotations. While recent Convolutional Neural …