To address the challenges of long-tailed classification, researchers have proposed several
approaches to reduce model bias, most of which assume that classes with few samples are …

Large-scale data from real-world usually follow a long-tailed distribution (ie, a few majority
classes occupy plentiful training data, while most minority classes have few samples) …

In this paper, our goal is to design a simple learning paradigm for long-tail visual
recognition, which not only improves the robustness of the feature extractor but also …

Few-shot object detection (FSOD) aims to expand an object detector for novel categories
given only a few instances for training. The few training samples restrict the performance of …

Long-tail learning has received significant attention in recent years due to the challenge it
poses with extremely imbalanced datasets. In these datasets, only a few classes (known as …

Abstract Whole Slide Images (WSIs) are usually gigapixel in size and lack pixel-level
annotations. The WSI datasets are also imbalanced in categories. These unique …

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 …

Deep learning based food recognition has achieved remarkable progress in predicting food
types given an eating occasion image. However, there are two major obstacles that hinder …

Deep neural networks have made huge progress in the last few decades. However, as the
real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be …

Unlike the case when using a balanced training dataset, the per-class recall (ie, accuracy) of
neural networks trained with an imbalanced dataset are known to vary a lot from category to …