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Found 8 papers, 1 papers with code
Learning Gibbs-regularized GANs with variational discriminator reparameterization
We propose a novel approach to regularizing generative adversarial networks (GANs) leveraging learned {\em structured Gibbs distributions}.
R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting
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
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.
Learning random-walk label propagation for weakly-supervised semantic segmentation
Large-scale training for semantic segmentation is challenging due to the expense of obtaining training data for this task relative to other vision tasks.
Deep Network Flow for Multi-Object Tracking
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.
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
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.
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Variational reaction-diffusion systems for semantic segmentation
A novel global energy model for multi-class semantic image segmentation is proposed that admits very efficient exact inference and derivative calculations for learning.
Efficient high dimensional maximum entropy modeling via symmetric partition functions
The application of the maximum entropy principle to sequence modeling has been popularized by methods such as Conditional Random Fields (CRFs).
