Exploring Vision Transformers for Fine-grained Classification
This work addresses fine-grained classification for computer vision applications, presenting an incremental improvement over existing Vision Transformer methods.
The paper tackles fine-grained image classification by proposing a multi-stage Vision Transformer framework that localizes informative regions using self-attention and attention-guided augmentations, achieving competitive results on benchmarks like CUB-200-2011 and Stanford Cars.
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.