Shot Fine Grained

Few-shot fine-grained recognition tackles the challenge of classifying objects into highly similar subcategories using only a limited number of examples. Current research focuses on developing robust models that address the inherent difficulties of limited data and subtle inter-class differences, employing techniques like multi-view encoding and fusion, saliency-aware distillation, and background suppression to improve accuracy. These advancements leverage both improved feature extraction and novel similarity metrics within various embedding spaces, including hyperbolic spaces, to enhance discriminative power. The resulting improvements have significant implications for applications requiring efficient classification of visually similar objects with limited training data, such as automated visual inspection or wildlife identification.