Danijel Skočaj, PhD

Head of the laboratory

Full Professor
+386 1 479 8225

Danijel Skočaj is a professor at the University of Ljubljana, Faculty of Computer and Information Science. He is the head of the Visual Cognitive Systems Laboratory. His main research interests lie in the fields of computer vision, machine learning, and cognitive robotics. In the framework of basic and applied research, he’s been developing and introducing new advanced methods of deep learning and computer vision for solving complex problems requiring processing of visual information. He is also interested in the ethical aspects of artificial intelligence, machine learning and robotics, and the influence of the development of these technologies on society. He’s been lecturing the courses from the fields of computer vision, cognitive robotics, and deep learning. He has led or collaborated in a number of projects from these research areas, such as EU projects, national research projects as well as industry-funded applied projects. Through the research and development applied projects he’s been facilitating the transfer of research findings into practical applications.

Research

The main research interests: computer vision, pattern recognition, deep learning, cognitive systems.

Visual anomaly detection

This research focuses on the development of unsupervised visual anomaly detection methods. Trained on anomaly-free samples only, these methods attempt to remove the need for a difficult acquisition of a diverse set of anomalous objects while aiming to match the performance of supervised methods.

Surface defect detection

Contains 2 subtopics
We are designing novel deep architectures for visual surface inspection. The developed methods allow specialization for large defect detection such as cracks, as well as smooth deformations on reflective surfaces like dents. The methods are learning-based and are thus robust, run realtime and are applicable to a wide range of real problems. Several of the methods are part of most advances surface inspection commercial systems.

Deep reinforcement learning for navigation

This research investigates the development of autonomous mobile robot navigation methods using deep reinforcement learning. Our methods aim to produce navigation policies which are learned completely in simulation and deployed on real robots.

Traffic-sign detection

We explore automation of traffic-sign inventory management using deep-learning models. Models such as Faster R-CNN and Mask R-CNN are improved and applied to traffic sign detection. Instead of specializing in automated detection for only several traffic sign categories we explore possibility of automating the detection of over 200 different traffic signs that are needed to automate the traffic-sign inventory management.

Main Projects

Current projects

MV4.0 - Data-driven framework for development of machine vision solutions

October 2021 - September 2024
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.

Computer Vision

January 2019 - December 2024
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.

SMASH - Machine learning for science and humanities postdoctoral program

July 2023 - June 2028
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.

Past projects

DAViMaR - Adaptive deep perception methods for autonomous surface vehicles

April 2020 - August 2023
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.

GOSTOP - Building Blocks, Tools and Systems for the Factories of the Future

November 2016 - January 2020
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.

TraPri - Tradition meets the future - computer vision and augmented reality for the preservation and promotion of natural and cultural heritage

April 2018 - August 2018
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.

CV4foot - Study and comparison of advanced computer vision methods for foot modelling in a real-world environment

April 2014 - September 2014
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.

ViAMaRo - Robust computer vision methods for autonomous water surface vehicles

May 2017 - April 2020
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.

MV4.0 - Data-driven framework for development of machine vision solutions

October 2021 - September 2024
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.

DIVID - Detection of inconsistencies in complex visual data using deep learning

July 2018 - December 2021
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.

HiMoDel - Learning, analysis, and detection of motion in the framework of a hierarchical compositional visual architecture

April 2011 - August 2014
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.

ViLLarD - Maintenance of large databases based on visual information using incremental learning

July 2014 - June 2017
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.

CogX - Cognitive Systems that Self-Understand and Self-Extend

January 2007 - December 2010
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.

Mobvis - Vision Technologies and Intelligent Maps for Mobile Attentive Interfaces in Urban Scenarios

May 2005 - April 2008
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 - Visiontrain - Marie Curie Research Training Network

May 2005 - April 2008
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.

CoSy - Cognitive Systems for Cognitive Assistants

September 2004 - August 2008
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.

CogVis - Cognitive Vision Systems

May 2001 - July 2004
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.

Selected Publications

Teaching

Teaching in 2022/23

Old courses

  • Introduction to computer science (Uvod v računalništvo)
  • Scientific skills 2 (Veščine v znanstvenem delu 2, 3.st.)
  • Production of multimedia content (Produkcija multimedijskih gradiv)
  • Artificial intelligence (Umetna inteligenca, 3.st.)
  • Algorithms and data structures 1 (Algoritmi in podatkovne strukture 1)
  • Distributed intelligent software technologies (Porazdeljene inteligentne programske tehnologije)
  • Data structures and algorithms (Podatkovne strukture in algoritmi (UL PeF))
  • Computer science (Računalništvo (UL FPP))

All information about the courses is provided on the internal pages of UL FRI.

Awards

  • 2023: Winners of the perception challange on 2th Cloth and Manipulation Challenge, part of 7th Robotic Grasping and Manipulation Competition of ICRA 2023
  • 2022: Award for exceptional scientific achievement in the Republic of Slovenia in the year 2022 (ARRS).
  • 2021: The award for one of ten most remarkable research achievements at the University of Ljubljana in the year 2021.
  • 2021: Journal of Intelligent Manufacturing Certificate of Achievement for One of 2020’s Top Downloaded JIM Research Articles.
  • 2021: Prometheus of science award for excellence in communication for 2020, Slovenian Science Foundation.
  • 2020: The Golden Plaque for exceptional contributions to the development of scientific, pedagogical or artistic endeavours, and for strengthening the reputation of the University of Ljubljana.
  • 2019: Special recognition for outstanding research achievement, Faculty of Computer and information science, UL.
  • 2013, 2017, 2019: Best paper in the Pattern recognition session award, International Electrotechnical and Computer Science Conference ERK 2013, 2017, 2019, Portorož, Slovenia.
  • 2012: Award for exceptional scientific achievement in the Republic of Slovenia in the year 2011 (ARRS).
  • 2002: Best PhD paper award, 11th International Electrotechnical and Computer Science Conference ERK 2002, Portorož, Slovenia.
  • 2002: Best paper award, 26th Workshop of the Austrian Association for Pattern Recognition (ÖAGM/AAPR), Graz, Austria.

Awards of my students:

  • 2021: Honourably mentioned for research work of postgraduate students at UL FRI (Vitjan Zavrtanik, Domen Rački)
  • 2020: Special award for research work of postgraduate students at UL, Faculty of Computer and Information Science (Matej Dobrevski, Vitjan Zavrtanik)
  • 2020: Prešeren Prize for Students of the University of Ljubljana for outstanding achievements in science and art (Jaka Šircelj)
  • 2013, 2019, 2021, 2022: Faculty Prešeren Prize, UL, Faculty of Computer and Information Science (Klemen Istenič, Kristian Žarn, Vid Rijavec, Valter Hudovernik)

Membership