Abstract: By using labeled source domain data to train supervised models, unsupervised domain adapted (UDA) semantic segmentation tries ... combined with Contrastive Learning (CL) for domain ...

Contrastive self-supervised learning has outperformed supervised pretraining ... We perform the evaluation for the following downstream tasks: linear classification (VOC), semantic segmentation (VOC ...

a semantic segmentation model, termed mask-recovering and interactive-feature-enhancing (MRIFE), is proposed for more efficient feature extraction and separation. Specifically, to address the visual ...

However, despite its significance, this problem remains rather unexplored, with a few exceptions that considered unsupervised semantic segmentation on small-scale ... that adopts a predetermined prior ...

Python has long had the “f-string” feature, for creating formatted strings from variable data. Now, with Python 3.14, there’s a “template string” type — an f-string-like construction ...

A key feature of our method is the application of semantic segmentation techniques, enabling the automated categorization of micrograph pixels into distinct classifications of particle and background.

Methods: We utilizes Attention U-Net to recognize tooth descriptors, crops regions of interest (ROIs) based on the center of mass of these descriptors, and applies an integrated deep learning method ...