Fine Grained Visual Recognition

Fine-grained visual recognition (FGVR) focuses on classifying objects into highly similar subcategories, a challenging task due to subtle visual differences. Current research emphasizes improving FGVR performance in low-data regimes, employing techniques like data augmentation, self-supervised learning, and attention mechanisms to enhance feature extraction and model robustness. This involves leveraging both convolutional neural networks (CNNs) and vision transformers (ViTs), often incorporating novel modules for part-level feature analysis and multi-scale processing. Advances in FGVR have significant implications for various applications, including biodiversity monitoring, automated quality control, and medical image analysis.