Label-Smooth Learning for Fine-Grained Visual Categorization

Fine-Grained Visual Categorization (FGVC) is challenging due to the superior similarity among categories and the large within-category variance. Existing work tackles this problem by designing self-localization modules in an end-to-end DCNN to learn semantic part features. However the model efficiency of this strategy decreases significantly with the increasing of the number of categories, because more parts are needed to offset the impact of the increasing of categories. In this paper, we propose a label-smooth learning method that improves models applicability to large categories by maximizing its prediction diversity. Based on the similarity among fine-grained categories, a KL divergence between uniform and prediction distributions is established to reduce model’s confidence on the ground-truth category, while raising its confidence on similar categories. By minimizing it, information from similar categories are exploited for model learning, thus diminishing the effects caused by the increasing of categories. Experiments on five benchmark datasets of mid-scale (CUB-200-2011, Stanford Dogs, Stanford Cars, and FGVC-Aircraft) and large-scale (NABirds) categories show a clear advantage of the proposed label-smooth learning and demonstrate its comparable or state-of-the-art performance. Code is available at https://github.com/Cedric-Mo/LS-for-FGVC.