To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

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 …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

In this work, we study the continual semantic segmentation problem, where the deep neural
networks are required to incorporate new classes continually without catastrophic forgetting …

Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …

In this paper, we study the application of Test-time domain adaptation in semantic
segmentation (TTDA-Seg) where both efficiency and effectiveness are crucial. Existing …

In this paper we make the first attempt at achieving the cross-modal (ie image-to-events)
adaptation for event-based object recognition without accessing any labeled source image …

This paper addresses an interesting yet challenging problem--source-free unsupervised
domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation--given only a …

In this work, we introduce a new concept, named source-free open compound domain
adaptation (SF-OCDA), and study it in semantic segmentation. SF-OCDA is more …

Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …