Fine-grained image recognition (FGIR) is a challenging task as it requires the recognition of
sub-categories with subtle differences. Recently, the swin transformer has shown impressive …

Facial Expression Recognition (FER) in the wild poses significant challenges due to realistic
occlusions, illumination, scale, and head pose variations of the facial images. In this article …

Recently, vision transformers (ViTs) have been investigated in fine-grained visual
recognition (FGVC) and are now considered state of the art. However, most ViT-based works …

Over the past few years, a significant progress has been made in deep convolutional neural
networks (CNNs)-based image recognition. This is mainly due to the strong ability of such …

In the field of Fine-Grained Visual Recognition (FGVR), the ability to resolve minute and
often subtle differences between highly similar object categories is paramount. The advent …

We study a crucial yet often overlooked issue inherent to Vision Transformers (ViTs): feature
maps of these models exhibit grid-like artifacts (“Original features” in Fig. 1), which hurt the …

Fine-grained image classification is a challenging computer vision task where various
species share similar visual appearances, resulting in misclassification if merely based on …

Fine-Grained Visual Classification (FGVC) is the task that requires recognizing the objects
belonging to multiple subordinate categories of a super-category. Recent state-of-the-art …

Fine-grained visual classification is a challenging task due to the high similarity between
categories and distinct differences among data within one single category. To address the …

Visual classification can be divided into coarse-grained and fine-grained classification.
Coarse-grained classification represents categories with a large degree of dissimilarity, such …