The real-world data tends to be heavily imbalanced and severely skew the data-driven deep
neural networks, which makes Long-Tailed Recognition (LTR) a massive challenging task …

Real-world data usually confronts severe class-imbalance problems, where several majority
classes have a significantly larger presence in the training set than minority classes. One …

The prevalence of long-tailed distributions in real-world data often results in classification
models favoring the dominant classes, neglecting the less frequent ones. Current …

Abstract Vision Transformer (ViT) has emerged as a prominent architecture for various
computer vision tasks. In ViT we divide the input image into patch tokens and process them …

Regression tasks in computer vision such as age estimation or counting are often formulated
into classification by quantizing the target space into classes. Yet real-world data is often …

Long-tailed imbalance distribution is a common issue in practical computer vision
applications. Previous works proposed methods to address this problem which can be …

There is growing interest in the challenging visual perception task of learning from long-
tailed class distributions. The extreme class imbalance in the training dataset biases the …

The long-tailed distribution is a common phenomenon in the real world. Extracted large
scale image datasets inevitably demonstrate the long-tailed property and models trained …

Thanks to the advances in deep learning-based computer vision, image classification has
shown great achievements. However, it has faced a heavy class imbalance issue which is …

Recent long-tailed classification methods generally adopt the two-stage pipeline and focus
on learning the classifier to tackle the imbalanced data in the second stage via re-sampling …