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 …

Human reasoning can be understood as a cooperation between the intuitive, associative"
System-1''and the deliberative, logical" System-2''. For existing System-1-like methods in …

Causal inference has become a powerful tool to handle the out-of-distribution (OOD)
generalization problem, which aims to extract the invariant features. However, conventional …

Recently, multi-expert methods have led to significant improvements in long-tail recognition
(LTR). We summarize two aspects that need further enhancement to contribute to LTR …

Representation learning for images has been advanced by recent progress in more complex
neural models such as the Vision Transformers and new learning theories such as the …

Abstract Conditional Generative Adversarial Networks (CGANs) are well developed on
balanced datasets as default standards for generating high-quality images of expected …

In real-world scenarios, where knowledge distributions exhibit long-tail. Humans manage to
master knowledge uniformly across imbalanced distributions, a feat attributed to their …

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 …

Deep learning-based image classification networks heavily rely on the extracted features.
However, as the model becomes deeper, important features may be lost, resulting in …

Although Domain Generalization (DG) problem has been fast-growing in the 2D image
tasks, its exploration on 3D point cloud data is still insufficient and challenged by more …