Visual Based Class

Visual-based class research focuses on improving the accuracy and robustness of image classification models, particularly when dealing with a large number of classes or significant data imbalances. Current efforts concentrate on adapting pre-trained vision-language models, employing techniques like prompt tuning and exploring multi-modality approaches to leverage information from diverse data sources (e.g., MRI and CT scans). These advancements are crucial for enhancing the reliability of AI systems in various applications, from medical image analysis to autonomous driving, by addressing challenges like misclassification and improving generalization to unseen data.