Vision transformers are nowadays the de-facto choice for image classification
tasks. There are two broad categories of classification tasks, fine-grained and
coarse-grained. In fine-grained classification, the necessity is to discover
subtle differences due to the high level of similarity between sub-classes.
Such distinctions are often lost as we downscale the image to save the memory
and computational cost associated with vision transformers (ViT). In this work,
we present an in-depth analysis and describe the critical components for
developing a system for the fine-grained categorization of plants from
herbarium sheets. Our extensive experimental analysis indicated the need for a
better augmentation technique and the ability of modern-day neural networks to
handle higher dimensional images. We also introduce a convolutional transformer
architecture called Conviformer which, unlike the popular Vision Transformer
(ConViT), can handle higher resolution images without exploding memory and
computational cost. We also introduce a novel, improved pre-processing
technique called PreSizer to resize images better while preserving their
original aspect ratios, which proved essential for classifying natural plants.
With our simple yet effective approach, we achieved SoTA on Herbarium 202x and
iNaturalist 2019 dataset.

该论文提出了一种新的分类方法，使用 Conviformer 和 PreSizer 处理高维度植物图像，实现细粒度植物图像分类，最终在 Herbarium 和 iNaturalist 数据集上取得了 SoTA 效果。

Conviformers: 基于卷积引导的视觉 Transformer

Conviformers: Convolutionally guided Vision Transformer

Herbarium sheets are invaluable for botanical research, and considerable time
and effort is spent by experts to label and identify specimens on them. In view
of recent advances in computer vision and deep learning, developing an
automated approach to help experts identify specimens could significantly
accelerate research in this area. Whereas most existing botanical datasets
comprise photos of specimens in the wild, herbarium sheets exhibit dried
specimens, which poses new challenges. We present a challenge dataset of
herbarium sheet images labeled by experts, with the intent of facilitating the
development of automated identification techniques for this challenging
scenario.

本研究旨在开发一种自动化方法，利用计算机视觉和深度学习技术帮助专家识别标本以加速植物学研究。为此，提供了一个由专家标记的干燥标本图像数据集用于挑战性场景的自动化识别的发展。