80 citations · 268 across the 17 of their papers we have counts for
25 papers
Multi-source Few-shot Domain Adaptation
Xiangyu Yue, Zangwei Zheng, Colorado Reed +3
Multi-source Domain Adaptation (MDA) aims to transfer predictive models from multiple, fully-labeled source domains to an unlabeled target domain. However, in many applications, re…
Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data
Hari Prasanna Das, Ryan Tran, Japjot Singh +4
At the onset of a pandemic, such as COVID-19, data with proper labeling/attributes corresponding to the new disease might be unavailable or sparse. Machine L…
Scene-aware Learning Network for Radar Object Detection
Zangwei Zheng, Xiangyu Yue, Kurt Keutzer +1
Object detection is essential to safe autonomous or assisted driving. Previous works usually utilize RGB images or LiDAR point clouds to identify and localize multiple objects in s…
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation
Xiangyu Yue, Zangwei Zheng, Shanghang Zhang +4
Unsupervised Domain Adaptation (UDA) transfers predictive models from a fully-labeled source domain to an unlabeled target domain. In some applications, however, it is expensive ev…
Self-Supervised Pretraining Improves Self-Supervised Pretraining
Colorado J. Reed, Xiangyu Yue, Ani Nrusimha +9
While self-supervised pretraining has proven beneficial for many computer vision tasks, it requires expensive and lengthy computation, large amounts of data, and is sensitive to da…
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
Jay Shenoy, Edward Kim, Xiangyu Yue +4
Safely interacting with humans is a significant challenge for autonomous driving. The performance of this interaction depends on machine learning-based modules of an autopilot, suc…