17 citations · 75 across the 11 of their papers we have counts for
12 papers
FOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection
Anqi Joyce Yang, James Tu, Nikita Dvornik +2
In order to navigate complex traffic environments, self-driving vehicles must recognize many semantic classes pertaining to vulnerable road users or traffic control devices. Howeve…
Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
Jay Sarva, Jingkang Wang, James Tu +3
Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate h…
Learning Realistic Traffic Agents in Closed-loop
Chris Zhang, James Tu, Lunjun Zhang +3
Realistic traffic simulation is crucial for developing self-driving software in a safe and scalable manner prior to real-world deployment. Typically, imitation learning (IL) is use…
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI
Alex Chang, Vinith Suriyakumar, Abhishek Moturu +5
Modern deep unsupervised learning methods have shown great promise for detecting diseases across a variety of medical imaging modalities. While previous generative modeling approac…
Diverse Complexity Measures for Dataset Curation in Self-driving
Abbas Sadat, Sean Segal, Sergio Casas +4
Modern self-driving autonomy systems heavily rely on deep learning. As a consequence, their performance is influenced significantly by the quality and richness of the training data…
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving
James Tu, Huichen Li, Xinchen Yan +6
Modern self-driving perception systems have been shown to improve upon processing complementary inputs such as LiDAR with images. In isolation, 2D images have been found to be extr…