The field of few-shot learning and fine-grained image classification is moving towards developing more robust and adaptable models that can effectively handle limited training data and novel classes. Researchers are exploring innovative approaches to address the challenges of catastrophic forgetting, overfitting, and noise robustness in few-shot learning. Some notable trends include the use of ensemble methods, dual-vision adaptation, and consistency-driven calibration to improve model performance and generalization. Noteworthy papers in this area include the proposal of a tripartite weight-space ensemble method, which enables seamless updates of the entire model with a few examples, and the introduction of a consistency-driven calibration and matching framework that mitigates knowledge conflict in few-shot class-incremental learning. Another significant contribution is the development of a hierarchical mask-enhanced dual reconstruction network for few-shot fine-grained image classification, which integrates dual-layer feature reconstruction with mask-enhanced feature processing to improve fine-grained classification.