Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack
performance when faced with out-of-distribution data, a common scenario due to the …

Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Abstract Three-dimensional (3D) cross-modality cardiac image segmentation is critical for
cardiac disease diagnosis and treatment. However, it confronts the challenge of modality …

Demographic biases and social stereotypes are common in pretrained language models
(PLMs), and a burgeoning body of literature focuses on removing the unwanted …

Single domain generalization aims to learn a model from a single training domain (source
domain) and apply it to multiple unseen test domains (target domains). Existing methods …

Remote photoplethysmography (rPPG) technology has drawn increasing attention in recent
years. It can extract Blood Volume Pulse (BVP) from facial videos, making many applications …

Abstract Deep Neural Networks have exhibited considerable success in various visual tasks.
However, when applied to unseen test datasets, state-of-the-art models often suffer …

Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of
distribution test domain samples. In this setting, the model can only access online unlabeled …

Abstract Domain generalization (DG) aims to learn a model that generalizes well to unseen
target domains utilizing multiple source domains without re-training. Most existing DG works …