2 citations · 4 across the 4 of their papers we have counts for
4 papers
Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
Jingyi Xu, Weidong Yang, Lingdong Kong +4
Unsupervised domain adaptation (UDA) is vital for alleviating the workload of labeling 3D point cloud data and mitigating the absence of labels when facing a newly defined domain.…
Zero-Shot Object Counting with Language-Vision Models
Jingyi Xu, Hieu Le, Dimitris Samaras
Class-agnostic object counting aims to count object instances of an arbitrary class at test time. It is challenging but also enables many potential applications. Current methods re…
Learning from Pseudo-labeled Segmentation for Multi-Class Object Counting
Jingyi Xu, Hieu Le, Dimitris Samaras
Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing, based on only a few…
Training Classifiers that are Universally Robust to All Label Noise Levels
Jingyi Xu, Tony Q. S. Quek, Kai Fong Ernest Chong
For classification tasks, deep neural networks are prone to overfitting in the presence of label noise. Although existing methods are able to alleviate this problem at low noise le…