Existing fine-grained visual categorization (FGVC) methods assume that the fine-grained semantics rest in the informative parts of an image. This assumption works well on favorable front-view object-centric images, but can face great challenges in many real-world scenarios, such as scene-centric images (e.g., street view) and adverse viewpoint (e.g., object reidentification, remote sensing). In such scenarios, the mis-/over-feature activation is likely to confuse the part selection and degrade the fine-grained representation. In this paper, we are motivated to design a universal FGVC framework for real-world scenarios. More precisely, we propose a concept guided learning (CGL), which models concepts of a certain fine-grained category as a combination of inherited concepts from its subordinate coarse-grained category and discriminative concepts from its own. The discriminative concepts is utilized to guide the fine-grained representation learning. Specifically, three key steps are designed, namely, concept mining, concept fusion, and concept constraint. On the other hand, to bridge the FGVC dataset gap under scene-centric and adverse viewpoint scenarios, a Fine-grained Land-cover Categorization Dataset (FGLCD) with 59,994 fine-grained samples is proposed. Extensive experiments show the proposed CGL: 1) has a competitive performance on conventional FGVC; 2) achieves state-of-the-art performance on fine-grained aerial scenes & scene-centric street scenes; 3) good generalization on object re-identification and fine-grained aerial object detection. The dataset and source code will be available at https://github.com/BiQiWHU/CGL.
