Abstract: Creating models that can effectively generalise across various tasks and adapt to test-time domain shifts is crucial. In my talk, I will introduce some of my latest work in improving generalization through Generative AI and test-time training. I’ll explore the intriguing question: “Do we still need real images for learning transferable visual representations?” and present our work that studies the use of synthetic data by training models using only images generated by Generative AI models. Additionally, I’ll demonstrate ways of effectively using these models to simulate test-time shifts such as changes in season, weather, or time of day, particularly for visual localization tasks, in order to improve model robustness to such known test-time shifts. The talk will conclude with our recent work on enhancing the robustness of Video Object Segmentation against test-time distribution shifts through test-time training.