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 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 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.

The functional objective of the project is to advance anomaly detection in images by developing multimodal models that integrate visual and linguistic information to not only detect if and where anomalies occur but also explain why. The main research goal is to create novel methods for semantic image understanding, zero-shot multimodal anomaly detection, and multimodal explanations that improve AI’s interpretability and transparency while reducing reliance on annotated data.

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.

This project is a continuation of the successful collaboration enjoyed between Poland, Germany and Portugal in the first SABUVIS project. In this first iteration, three different Biomimetic Underwater Vehicles (BUVs) were constructed with the resulting conclusion that BUVs can be designed with varying degrees of similarity with living organisms. Some of the identified benefits are that undulating propulsion consumes less electrical energy than conventional propulsion systems utilising screw based propellers, and different hydroacoustic signatures are produced with a corresponding lower noise level.

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.

The main goal of this project is to explore the topic of transparent object tracking, which has large potential for being used in practical applications as well as a lot of unanswered research questions. The two main challenges the project is focusing on are robust tracking in presence of distractors and segmentation of transparent objects in a video.

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 project primary goal is to develop the next-generation maritime environment perception methods, which will harvest the power of end-to-end trainable deep models for essential challenges of safe operation like: general obstacle detection with re-identification, implicit detection of hazardous areas and sensor fusion for improved detection.

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.

The objective of the project is to develop novel deep learning methods for modelling complex consistency and detecting inconsistencies in visual data using training images annotated with different levels of accuracy. The main project goal is to go beyond the traditional supervised learning, where all anomalies on all training images have to be adequately labelled.

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 aim of the GOSTOP programme was to accelerate the development of the Factories of the Future concept in Slovenia and to provide solutions to the current needs of Slovene industry. Our goal was to develop efficient machine vision algorithms, coupled with machine learning approaches, which would allow for fast and flexible adaptation of visual inspection systems to be able to deal with novel quality control problems.

The main goal of the project is to develop a framework for semi-supervised interactive incremental learning as well as specific methods for visual learning and recognition that will increase the quality and efficiency of large visual information databases maintenance.

In this student project we were exploring the potential of using computer vision techniques for footwear recommendation systems. The maingoal was to improve existing methods with advanced computer vision technologies, to solve the problem of automatic feet modelling, and to determine the suitability of the latest mobile devices for such advanced computer vision algorithms.

A six month student project funded by Slovenian initiative Creative approach to practical knowledge. The goal was to test several computer vision algorithms for autonomous navigation in a USV built by Harpha Sea Koper, while working as a team, distributing administration work, jointly writing reports, presentations and papers. This gave the students an opportunity to gain practical engineering skills as well as team-work competences.

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 use of computer vision makes for a very intuitive interaction with mobile device, greatly simplifying it. We developed computer vision methods suitable for mobile devices, and use them to implement designed interaction scenarios in prototype applications.

POETICON was a research project in the Seventh framework programme, that explored the "poetics of everyday life", i.e. the synthesis of sensorimotor representations and natural language in everyday human interaction.

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.

The main objective in MOBVIS was to achieve a theoretical and practical leap in the application of artificial vision in smart mobile applications with a primary focus in spatial awareness and guidance. In order to achieve this goal, MOBVIS concentrated its research on the integration of multi-modal context awareness, vision based object recognition, and intelligent map technology, into an innovative form of an attentive interface, which enables perception and reasoning on a vast amount of data and in a continuously operating framework.

Visiontrain was a Marie Curie Research Training Network Project that addressed the problem of understanding vision from both computational and cognitive points of view. The research approach was based on formal mathematical models and on the thorough experimental validation of these models.

The main goal of this EU FP6 project was to advance the science of cognitive systems through a multi-disciplinary investigation of requirements, design options and trade-offs for human-like, autonomous, integrated, physical (eg., robot) systems, including requirements for architectures, for forms of representation, for perceptual mechanisms, for learning, planning, reasoning and motivation, for action and communication.

The main objective of this EU FP5 project CogVis was to provide the methods and techniques that enable construction of vision systems that can perform task oriented categorization and recognition of objects and events in the context of an embodied agent.