Abstract Recently lightweight Vision Transformers (ViTs) demonstrate superior performance
and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on …

Recently, the scale of transformers has grown rapidly, which introduces considerable
challenges in terms of training overhead and inference efficiency in the scope of task …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

In the past few years, large-scale pre-trained vision-language models like CLIP have
achieved tremendous success in various fields. Naturally, how to transfer the rich knowledge …

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of
safety assurance. However objects encountered on the road exhibit a long-tailed distribution …

3D object detection has recently received much attention due to its great potential in
autonomous vehicle (AV). The success of deep learning based object detectors relies on the …

Annotating datasets for object detection is an expensive and time-consuming endeavor. To
minimize this burden active learning (AL) techniques are employed to select the most …

Recent advancements in autonomous driving, augmented reality, robotics, and embodied
intelligence have necessitated 3D perception algorithms. However, current 3D perception …

Abstract Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a
related target domain. This paper investigates a practical DA paradigm, namely Source data …