Cross Domain Few Shot

Cross-domain few-shot learning tackles the challenge of training machine learning models to perform well on new, unseen data domains using only a limited number of labeled examples. Current research focuses on adapting existing models, such as those based on Faster R-CNN or the Segment Anything Model (SAM), through techniques like feature transformation, pseudo-support set generation, and task-adaptive prompting to bridge the domain gap. This area is significant because it addresses the limitations of traditional deep learning approaches that require massive datasets, paving the way for more robust and efficient AI systems in various applications, including medical image analysis, traffic sign recognition, and remote sensing.