A Data Augmentation Based ViT for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) is a fundamental and longstanding problem aiming to recognize objects belonging to different subclasses accurately. Unfortunately, since categories are often confused, this task is genuinely challenging. Most previous methods solve this problem in two main ways: adding more annotations or constructing more complex structures. These approaches, however, require expensive labels or sophisticated designs. To alleviate these constraints, in this work, we propose an easy but efficient method called DA-ViT, just using data augmentations to supervise the model. Specifically, we adopt a vision transformer as the backbone. Then, we introduce highly interpretable visual heatmaps to guide the targeted data augmentations, and three methods (local area enlargement, flipping, and cutout) are created based on the high-response areas. Furthermore, the margins among confusing classes can be increased by simply using label smoothing. Extensive experiments conducted on three popular fine-grained benchmarks demonstrate that we achieve SOTA performance. Meanwhile, during the inference, our method requires less computational burden.