Search Results for author: Paul Vernaza

Found 8 papers, 1 papers with code

Learning Gibbs-regularized GANs with variational discriminator reparameterization

no code implementations27 Sep 2018 Nicholas Rhinehart, Anqi Liu, Kihyuk Sohn, Paul Vernaza

We propose a novel approach to regularizing generative adversarial networks (GANs) leveraging learned {\em structured Gibbs distributions}.

Trajectory Forecasting

R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting

no code implementations ECCV 2018 Nicholas Rhinehart, Kris M. Kitani, Paul Vernaza

We propose a method to forecast a vehicle's ego-motion as a distribution over spatiotemporal paths, conditioned on features (e. g., from LIDAR and images) embedded in an overhead map.

Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences

no code implementations ECCV 2018 Mohammed E. Fathy, Quoc-Huy Tran, M. Zeeshan Zia, Paul Vernaza, Manmohan Chandraker

Further, we propose to use activation maps at different layers of a CNN, as an effective and principled replacement for the multi-resolution image pyramids often used for matching tasks.

Geometric Matching Metric Learning +1

Learning random-walk label propagation for weakly-supervised semantic segmentation

no code implementations CVPR 2017 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.

Weakly-Supervised Semantic Segmentation

Deep Network Flow for Multi-Object Tracking

no code implementations CVPR 2017 Samuel Schulter, Paul Vernaza, Wongun Choi, Manmohan Chandraker

In this work, we demonstrate that it is possible to learn features for network-flow-based data association via backpropagation, by expressing the optimum of a smoothed network flow problem as a differentiable function of the pairwise association costs.

Graph Matching Multi-Object Tracking

DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

3 code implementations CVPR 2017 Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B. Choy, Philip H. S. Torr, Manmohan Chandraker

DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i. e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents.

Future prediction Multi Future Trajectory Prediction +1

Variational reaction-diffusion systems for semantic segmentation

no code implementations1 Apr 2016 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.

Semantic Segmentation

Efficient high dimensional maximum entropy modeling via symmetric partition functions

no code implementations NeurIPS 2012 Paul Vernaza, Drew Bagnell

The application of the maximum entropy principle to sequence modeling has been popularized by methods such as Conditional Random Fields (CRFs).

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