Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

This article presents a comprehensive survey of online test-time adaptation (OTTA), focusing
on effectively adapting machine learning models to distributionally different target data upon …

Abstract Complementary Label Learning (CLL), a crucial aspect of weakly supervised
learning, has seen significant theoretical and practical advancements. However, a …

Continual test-time domain adaptation (TTA) is a challenging topic in the field of source-free
domain adaptation, which focuses on addressing cross-domain multimedia information …

LiDAR-based 3D object detection is pivotal across many applications, yet the performance
of such detection systems often degrades after deployment, especially when faced with …

Deep neural networks have significantly advanced various fields. However, these models
often encounter difficulties in achieving effective generalization when the distribution of test …

Complementary-label learning is a weakly supervised learning problem in which each
training example is associated with one or multiple complementary labels indicating the …

LiDAR-based 3D object detection has seen impressive advances in recent times. However,
deploying trained 3D detectors in the real world often yields unsatisfactory performance …

In recent years, zero-shot learning has attracted the focus of many researchers, due to its
flexibility and generality. Many approaches have been proposed to achieve the zero-shot …

The Domain gap is an important issue when applying AI-based approaches to clinical use.
Many recent approaches introduce domain adaptation (DA) to eliminate the influence of the …