Pro-NeXt: An All-in-One Unified Model for General Fine-Grained Visual Recognition

Abstract

Unlike general visual classification (CLS) tasks, certain CLS problems are significantly more challenging as they involve recognizing professionally categorized or highly specialized images. Fine-Grained Visual Classification (FGVC) has emerged as a broad solution to address this complexity. However, most existing methods have been predominantly evaluated on a limited set of homogeneous benchmarks, such as bird species or vehicle brands. Moreover, these approaches often train separate models for each specific task, which restricts their generalizability. This paper proposes a scalable and explainable foundational model designed to tackle a wide range of FGVC tasks from a unified and generalizable perspective. We introduce a novel architecture named Pro-NeXt and reveal that Pro-NeXt exhibits substantial generalizability across diverse professional fields such as fashion, medicine, and art areas, previously considered disparate. Our basic-sized Pro-NeXt-B surpasses all preceding task-specific models across 12 distinct datasets within 5 diverse domains. Furthermore, we find its good scaling property that scaling up Pro-NeXt in depth and width with increasing GFlops can consistently enhance its accuracy. Beyond scalability and adaptability, the intermediate features of Pro-NeXt achieve reliable object detection and segmentation performance without extra training, highlighting its solid explainability. We will release the code to promote further research in this area.