Fine Grained Image Classification

Fine-grained image classification focuses on distinguishing subtle visual differences between subordinate categories within a broader class, a task challenging for even advanced deep learning models. Current research emphasizes improving model performance in low-data regimes through techniques like self-supervised learning, data augmentation, and the integration of multi-modal information (e.g., text descriptions, geographical data). These advancements are crucial for applications ranging from automated disease diagnosis in medical imaging to improved object recognition in various fields, ultimately enhancing the accuracy and efficiency of computer vision systems.