Fine-Grained Visual Perception

Fine-grained visual perception is about recognizing details that broad scene understanding can miss, such as small attributes, object parts, subtle differences between images, and the locations of specific objects. Approaches include part- and attribute-based representations, hierarchical classification, dense visual-language alignment, and adaptive region selection that zooms or searches for task-relevant evidence. Benchmarks test fine-grained recognition, high-resolution question answering, open-vocabulary detection, visual grounding, counting, and controlled image comparisons across domains such as vehicles, food, medicine, and aerial imagery. A central challenge is that strong language and general vision abilities do not guarantee reliable use of local visual evidence: models may overlook details or rely on semantic shortcuts, while added resolution, model scale, or reasoning does not consistently solve the problem.