Gaussian Process Priors for View-Aware Inference

Authors

  • Yuxin Hou Aalto University
  • Ari Heljakka Aalto University GenMind Ltd.
  • Arno Solin Aalto University

Keywords:

Bayesian Learning

Abstract

While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations between frames have received less attention. Even though probabilistic machine learning provides the ability to encode correlation as prior knowledge for inference, there is a tangible gap between the theory and practice of applying probabilistic methods to modern vision problems. For this, we derive a principled framework to combine information coupling between camera poses (translation and orientation) with deep models. We proposed a novel view kernel that generalizes the standard periodic kernel in SO(3). We show how this soft-prior knowledge can aid several pose-related vision tasks like novel view synthesis and predict arbitrary points in the latent space of generative models, pointing towards a range of new applications for inter-frame reasoning.

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Published

2021-05-18

How to Cite

Hou, Y., Heljakka, A., & Solin, A. (2021). Gaussian Process Priors for View-Aware Inference. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 7762-7770. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16948

Issue

Section

AAAI Technical Track on Machine Learning II