Search Results for author:
Found 5 papers, 2 papers with code
Learning What Not to Model: Gaussian Process Regression with Negative Constraints
But, what if for an input point '$\bar{\mathbf{x}}$', we want to constrain the GP to avoid a target regression value '$\bar{y}(\bar{\mathbf{x}})$' (a negative datapair)?
Hierarchical Video Prediction Using Relational Layouts for Human-Object Interactions
Our work builds on hierarchical video prediction models, which disentangle the video generation process into two stages: predicting a high-level representation, such as pose sequence, and then learning a pose-to-pixels translation model for pixel generation.
Diverse Video Generation using a Gaussian Process Trigger
Our approach, Diverse Video Generator, uses a Gaussian Process (GP) to learn priors on future states given the past and maintains a probability distribution over possible futures given a particular sample.
Ranked #1 on
Video Prediction
on KTH
(Diversity metric)
Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements
In this paper, we present a novel robust framework for low-level vision tasks, including denoising, object removal, frame interpolation, and super-resolution, that does not require any external training data corpus.
