Shallow models meet deep vision

Project Details

Description

This project is concerned with perceiving, understanding, and representing objects and their environment. This constitutes key challenges for any autonomous system or augmented reality setup, especially when these functions are performed under uncertainty. The project combines statistical machine learning and computer vision to develop new methods for data-driven 3D visual computing, uncertainty quantification, and online learning. We put special interest in advancing the state-of-the-art in vision and sensor fusion methods, and in developing methods capable of inferring location, pose, and semantics from visual data under uncertainty. This is a crossover of the fields of computer vision and statistical machine learning, and aim at renewing the view on how the two can be combined.
Short titleSHADE/Solin
StatusActive
Effective start/end date01/09/201931/08/2023

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 13 - Climate Action

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Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
  • Combining pseudo-point and state space approximations for sum-separable Gaussian processes

    Tebbutt, W., Solin, A. & Turner, R. E., 2021, Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence. p. 1607-1617 (Proceedings of Machine Learning Research ; vol. 161).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
    File
    2 Downloads (Pure)
  • Gaussian Process Priors for View-Aware Inference

    Hou, Y., Heljakka, A. & Solin, A., 2021, THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE. AAAI, p. 7762-7770 9 p. (AAAI Conference on Artificial Intelligence; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
  • Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression

    Yu, Z., Zhu, M., Trapp, M., Skryagin, A. & Kersting, K., 2021, Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. p. 2008-2018 11 p. (Proceedings of Machine Learning Research ; vol. 161).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
    File
    2 Downloads (Pure)