This ARIS funded project investigats the relationship between sensor fusion and self-supervised learning for data-driven Earth Observation. We focus on the role of self-supervised deep learning for sensor fusion from the perspective of different sources with different spatial resolutions and spectral coverage. The project is grounded in a real-world application in the field of hydrology, where the goal is to predict the water level in rivers using satellite and drone imagery.

This ESA funded project investigates the relationship between sensor fusion and self-supervised learning for data-driven Earth Observation. We focus on the role of self-supervised deep learning for sensor fusion from the perspective of different sources with different spatial resolutions and spectral coverage. The project is grounded in a real-world application in the field of hydrology, where the goal is to predict the water level in rivers using satellite and drone imagery.

This project aims to develop a novel motion understanding paradigm, centered on automatically determining the minimal scene understanding required to track one or multiple objects throughout a video. It tackles three core challenges: developing a few-shot object detector capable of identifying all objects in a category based on limited examples, tracking individual objects amid distractors, and extending this to track transformable objects in complex environments.

SMASH is an innovative, intersectoral, career-development training program for outstanding postdoctoral researchers, co-funded by the Marie Skłodowska-Curie Actions COFUND program. SMASH is open to researchers around the world who are interested in developing cutting-edge machine learning applications for science and humanities.

The functional objective of the project is to shift the paradigm in the development of machine vision solutions from hand-engineered specific solutions to data-driven learning-based design and development that would enable more general, efficient, flexible and economical development, deployment and maintenance of machine vision systems. The main research goal of this project is to develop novel deep learning methods for iterative, active, robust, weak, self-, unsupervised and few-shot learning that would reduce the amount of needed annotated data.

The challenge that we address in this project is a robust design of deep generative models, their training and application to a visual tracking scenario. We believe that a generative appearance model of the entire object is a crucial step towards grounding visual object tracking in high-level concepts behind raw pixel values.

Computer vision is becoming a focal problem area of artificial intelligence. On the wings of deep learning it has become very powerful tool for solving various problems involving processing of visual information. In the framework of this programme we are addressing several research questions ranging from visual tracking to visual learning for autonomous robots, with a special emphasis on going beyond supervised deep learning.

In this student project we have developed an innovative solution based on mobile computer vision and augmented reality, which presents the tradition of viticulture and wine growing in Vipava valley with the technology of the future. We have developed a prototype of an Android mobile application and a content management system that enable efficient and attractive communication of relevant information.

The project primary goal is to develop functionalities required for robust autonomous navigation of USVs in uncontrolled environments, primarily relying on the captured visual information. The project focuses on obstacle detection using monocular and stereo systems, development of efficient visual tracking algorithms for marine environments and environment representation through sensor fusion.

The project primary goal was to develop a holistic approach towards learning, detection and recognition / categorisation of the visual motion and the phenomena derived from it. The project explored the paradigm of learning multi­layer compositional hierarchies.

The challenge this project addressed was development of a methodology that would bridge the gap between the computer-centered low-level image features and the high-level human-centered semantic meanings. The methodology explored was hierarchical compositional models, enriched by discriminative information and extended to online learning.

The high level aim of this EU FP7 project was to develop a unified theory of self-understanding and self-extension with a convincing instantiation and implementation of this theory in a robot. By self-understanding we mean that the robot has representations of gaps in its knowledge or uncertainty in its beliefs. By self-extension we mean the ability of the robot to extend its own abilities or knowledge by planning learning activities and carrying them out. The project involved six universities and about 30 researchers.