Guided Domain Generalization

Guided domain generalization aims to improve the ability of machine learning models to generalize to unseen data by incorporating additional information, often textual descriptions, to guide the learning process. Current research focuses on leveraging this extra information to align features across different domains, mitigating biases inherent in visual data alone, and employing techniques like hierarchical attention mechanisms and prompt learning to effectively integrate text and image data. This approach shows promise in enhancing model robustness and performance, particularly in scenarios with limited training data, impacting fields like face recognition and potentially broader applications requiring generalization across diverse datasets.