Fine Grained Visual

Fine-grained visual categorization (FGVC) focuses on distinguishing subtle differences between visually similar objects within the same category, a challenge addressed through advanced deep learning techniques. Current research emphasizes improving model robustness to intra-class variation and inter-class similarity, employing architectures like Vision Transformers and Convolutional Neural Networks, often enhanced with contrastive learning, attention mechanisms, and novel loss functions. These advancements have significant implications for various applications, including automated species identification, product recognition, and medical image analysis, by enabling more accurate and reliable classification of highly similar objects.