The field of domain adaptation is moving towards more practical and challenging settings, such as source-free domain adaptation (SFDA) and multi-source active domain adaptation (MS-ADA). Researchers are exploring innovative approaches to address the limitations of traditional domain adaptation methods, including the use of attention mechanisms, contrast learning, and diffusion models. These advances aim to improve the performance of models in target domains without requiring access to source data and labels during adaptation. Noteworthy papers in this area include:

• Attention Residual Fusion Network with Contrast for Source-free Domain Adaptation, which proposes a novel framework to alleviate negative transfer and domain shift during adaptation.
• GALA: A GlobAl-LocAl Approach for Multi-Source Active Domain Adaptation, which explores a more practical and challenging setting to further enhance target-domain performance by selectively acquiring annotations from the target domain.
• Aligning What You Separate: Denoised Patch Mixing for Source-Free Domain Adaptation in Medical Image Segmentation, which presents a new SFDA framework that leverages Hard Sample Selection and Denoised Patch Mixing to progressively align target distributions.
• Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation, which proposes a novel generation-based framework that leverages unlabeled target data as references to reliably generate and progressively refine a pseudo-target domain for SFDA.