Self-supervised learning (SSL) strategies have demonstrated remarkable performance in
various recognition tasks. However, both our preliminary investigation and recent studies …

Fine-grained image recognition (FGIR), unlike traditional coarse-grained recognition, is
centered on distinguishing fine-level subclasses within broader semantic categories. It holds …

Image classification for real-world applications often involves complicated data distributions
such as fine-grained and long-tailed. To address the two challenging issues simultaneously …

Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease
(AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype …

Recent research on few-shot fine-grained image classification (FSFG) has predominantly
focused on extracting discriminative features. The limited attention paid to the role of loss …

Abstract Self-Supervised Learning (SSL) has become a prominent approach for acquiring
visual representations across various tasks, yet its application in fine-grained visual …

Nowadays, more and more brands use interesting anime characters to promote and
increase brand awareness. However, real-time and interesting promotional materials are …

Fine-grained visual classification aims to classify similar sub-categories with the challenges
of large variations within the same sub-category and high visual similarities between …

Remote-sensing (RS) images with high spatial and temporal resolutions play a significant
role in monitoring periodic landscape changes for earth observation science. To enrich RS …

Fine-grained visual classification (FGVC) is challenging due to the subtle inter-class
variations. Key region location and discriminative feature extraction are the crucial aspects …