The recent developments in the research area of domain generalization and out-of-distribution (OOD) detection have seen significant advancements, particularly in integrating fairness constraints and enhancing model adaptability across diverse domains. Innovations in meta-learning frameworks have enabled the disentanglement of latent data representations to improve generalization while maintaining fairness, addressing a critical gap in existing methods. Additionally, adaptive domain learning schemes have been introduced to tackle cross-domain challenges in image denoising, leveraging small amounts of target domain data to fine-tune models effectively. The field is also witnessing advancements in object detection and OOD detection, with novel algorithms that enhance robustness without requiring retraining, leveraging high-resolution feature maps and supervised dimensionality reduction techniques. Vision-language models are being further refined through prompt learning strategies that disentangle visual style and content, enhancing domain generalization and adaptation capabilities. These developments collectively push the boundaries of model robustness and adaptability, making significant strides towards more reliable and versatile machine learning applications in open-world scenarios.