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 …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained
on the source domain using only unlabeled online data from the target domain to alleviate …

Depth cues are known to be useful for visual perception. However, direct measurement of
depth is often impracticable. Fortunately, though, modern learning-based methods offer …

Most recent test-time adaptation methods focus on only classification tasks, use specialized
network architectures, destroy model calibration or rely on lightweight information from the …

In the last decade, many deep learning models have been well trained and made a great
success in various fields of machine intelligence, especially for computer vision and natural …

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich
source domain to an unlabeled target domain without seeing source data. While most …

Unsupervised domain adaptation deals with a task from an unlabeled target domain by
leveraging the knowledge gained from labeled source domain (s). The fuzzy system is …

Cost-effective depth and infrared sensors as alternatives to usual RGB sensors are now a
reality, and have some advantages over RGB in domains like autonomous navigation and …