The field of cross-domain adaptation and style transfer is rapidly advancing, with a focus on developing innovative methods to mitigate the style gap between different devices and domains. Recent research has explored the use of token-driven frameworks, ordinary differential equations, and diffusion-based approaches to improve the performance of downstream tasks. These methods aim to preserve source content while transferring the common style of the target domain, ensuring that content and style remain unblended. Notable papers in this area include TRUST, which proposes a token-driven dual-stream framework for ultrasound image style transfer, and ConstStyle, which leverages a unified domain to capture domain-invariant features and bridge the domain gap. Additionally, papers like DUDE and LADB have introduced novel approaches for unsupervised cross-domain image retrieval and semi-supervised domain translation, respectively.