Open Domain Generalization

Open-domain generalization (ODG) aims to train machine learning models that can accurately perform on unseen data drawn from both new categories and new domains, a crucial step towards robust AI. Current research focuses on developing methods that leverage meta-learning, self-supervised learning, and vision-language models (VLMs) to improve generalization capabilities, often employing techniques like prompt tuning and regularization to enhance model performance. The ability to create models that generalize across diverse and unpredictable data distributions has significant implications for various applications, including robotics, image understanding, and natural language processing, where encountering novel situations is commonplace.