The adaptive intra-class variation contrastive learning algorithm proposed for unsupervised person re-identification can be extended to other fine-grained visual recognition tasks by adapting the concept of curriculum learning and adaptive sample selection to different domains. For tasks such as fine-grained object recognition or species classification, the algorithm can be modified to evaluate the learning capability of the model for each class based on specific features relevant to the task. By considering the intra-class variations and selecting appropriate samples for training, the algorithm can help improve the model's performance in distinguishing subtle differences between similar classes. Additionally, the adaptive outlier filtering process can be applied to handle noisy or outlier data points that may exist in fine-grained visual recognition tasks, enhancing the model's robustness and generalization ability.