arXiv AI By Victor Enescu, Assaad Zeghina, Matthieu Meignin, Nicolas Viltard, C\'ecile Mallet

Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Machine Learning
Aug 26

Joint Distribution Alignment for Universal Domain Adaptation

The paper introduces Joint Distribution Alignment for Universal Domain Adaptation (JAUA), a new algorithm designed for scenarios where source and target domains have differing label spaces. It provides a theoretical upper bound on generalization error for Universal Domain Adaptation and proposes aligning joint distributions using Chi‑Square divergence, complemented by a progressive pseudo‑labeling strategy. Experiments on six public image datasets show JAUA outperforms existing methods in handling Universal Domain Adaptation challenges.

By Shizhe Li, Hongshan Pu, Mengying Xie, Yi Xiang, Xiaowei Yang
arXiv Computer Vision
Sep 24

ICM: Intra-class Mixing for Domain Adaptation in Adverse Weather

The paper introduces ICM, an Intra-Class Mixing Consistency framework for unsupervised domain adaptation in semantic segmentation under adverse weather. ICM mixes regions within the same image and semantic class to maintain realistic layouts, contrasting prior methods that combine across images or domains. On the Cityscapes → ACDC benchmark, ICM achieves 75.7% mIoU, surpassing previous state‑of‑the‑art results by 1.9 percentage points.

By Boying Li, Chang Liu, Britta Ayano Wilde, Gy\"orgy Kov\'acs, Tosin Adewumi, Bj\"orn Backe, Hamam Mokayed