Fine-grained Visual Classification with High-temperature Refinement and Background Suppression

Understand

Fine-grained visual classification is a challenging task due to the high similarity between categories and distinct differences among data within one single category.

• To address the challenges, previous strategies have focused on localizing subtle discrepancies between categories and enhencing the discriminative features in them.
• However, the background also provides important information that can tell the model which features are unnecessary or even harmful for classification, and models that rely too heavily on subtle features may overlook global features and contextual information.
• In this paper, we propose a novel network called ``High-temperaturE Refinement and Background Suppression'' (HERBS), which consists of two modules, namely, the high-temperature refinement module and the background suppression module, for extracting discriminative features and suppressing background noise, respectively.