Existing computer vision research in categorization struggles with
fine-grained attributes recognition due to the inherently high intra-class
variances and low inter-class variances. SOTA methods tackle this challenge by
locating the most informative image regions and rely on them to classify the
complete image. The most recent work, Vision Transformer (ViT), shows its
strong performance in both traditional and fine-grained classification tasks.
In this work, we propose a multi-stage ViT framework for fine-grained image
classification tasks, which localizes the informative image regions without
requiring architectural changes using the inherent multi-head self-attention
mechanism. We also introduce attention-guided augmentations for improving the
model's capabilities. We demonstrate the value of our approach by experimenting
with four popular fine-grained benchmarks: CUB-200-2011, Stanford Cars,
Stanford Dogs, and FGVC7 Plant Pathology. We also prove our model's
interpretability via qualitative results.

研究提出了一种多阶段的 Vision Transformer 框架，用于细粒度图像分类，可在不需要架构变化的情况下定位信息图像区域。 Attention-guided 增强技术提高了模型性能，并在四个流行的细粒度基准测试中进行了实验，同时通过定性结果证明了模型的可解释性。