219 citations · 294 across the 5 of their papers we have counts for
6 papers
DriveGPT: Scaling Autoregressive Behavior Models for Driving
Xin Huang, Eric M. Wolff, Paul Vernaza +13
We present DriveGPT, a scalable behavior model for autonomous driving. We model driving as a sequential decision-making task, and learn a transformer model to predict future agent…
Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences
Mohammed E. Fathy, Quoc-Huy Tran, M. Zeeshan Zia +2
Interest point descriptors have fueled progress on almost every problem in computer vision. Recent advances in deep neural networks have enabled task-specific learned descriptors t…
Learning random-walk label propagation for weakly-supervised semantic segmentation
Paul Vernaza, Manmohan Chandraker
Large-scale training for semantic segmentation is challenging due to the expense of obtaining training data for this task relative to other vision tasks. We propose a novel trainin…
Deep Network Flow for Multi-Object Tracking
Samuel Schulter, Paul Vernaza, Wongun Choi +1
Data association problems are an important component of many computer vision applications, with multi-object tracking being one of the most prominent examples. A typical approach t…
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Namhoon Lee, Wongun Choi, Paul Vernaza +3
We introduce a Deep Stochastic IOC RNN Encoderdecoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE effectively pre…
Variational reaction-diffusion systems for semantic segmentation
Paul Vernaza
A novel global energy model for multi-class semantic image segmentation is proposed that admits very efficient exact inference and derivative calculations for learning. Inference i…