Computer Vision and Learning Systems (CVL)

Welcome to Computer Vision and Learning systems (CVL), part of the Department of Electrical engineering at Linköping University.

Robot and researcher.Foto: Göran Billeson

The field of computer vision is a sub area of AI, and it has its roots in the modeling of the human visual system (HVS).

It is commonly accepted that about 80% of what we perceive is vision-based (DOI 10.3233/NRE-2010-0599), but modeling vision is a systematically underestimated scientific challenge - an implication of Moravec’s paradox, “We're least aware of what our minds do best” (Minsky 1986).

The highly intuitive nature of the HVS makes it difficult for us to understand the myriad of interdisciplinary problems associated with computer vision.

The research at Computer Vision and Learning Systems (CVL) has a strong focus on theory and methods, in particular within machine learning, signal processing, and applied mathematics. The resulting methods are applied in fields where technical systems are supposed to coexist with and therefore predict actions of humans. Self-driving cars sharing road space and interacting with humans, sustainable forestry and agriculture, monitoring of greenhouse gases as well as classification and monitoring of animals are some application domains.

CVL's research topics cover a wide range of challenges within machine learning for computer vision and robot perception:

  • Continuous-time modeling of 3D motion
  • Estimation of pose and 3D structure
  • Few-shot and weakly supervised learning
  • Geometric deep learning
  • Human and animal motion analysis
  • Medical imaging and analysis
  • Quantum machine learning
  • Reinforcement learning
  • Remote sensing and data analysis
  • Semi-supervised and incremental learning
  • Scene flow estimation
  • Uncertainty representation
  • Video and semantic segmentation
  • Vision for action

'He who loves practice without theory is like the sailor who boards ship without a rudder and compass and never knows where he may cast.'
Leonardo da Vinci (1452-1519)

Courses

Courses given by the Computer Vision and Learning Systsems

Specialisations

Specialisation in Computer Vision and Signal Analysis

Specialisation in Mathematical Computer Vision

Thesis

Master Thesis Projects in Computer Vision and Learning Systems

Contact

Research within WASP Computer Vision Laboratory

Other research collaborations

News

Presentation by Lotten Juhlin.

02 September 2025

ISY Day 2025 – sustainability in academia, industry, and society

This years theme for the ISY Day was “Sustainability in Academia, Industry, and Society”, featuring lectures, discussions, and examples of how sustainability can be integrated into research, education, and industrial development.

Tomas Landelius and Carolina Natel de Moura.

21 November 2024

The focus period resulted in new collaborations for the climate

In the fall of 2024, researchers from around the world once again gathered at Linköping University for ELLIIT's five-week focus period. This time, the goal was to initiate and deepen collaborations in climate research using machine learning.

Participants are listening to a lecture.

15 October 2024

Symposium aiming to improve the climate

In the fall of 2024, Linköping University once again hosted ELLIIT's five-week-long focus period. This guest researcher program aimed for greater breadth in interdisciplinarity this year, with the theme of machine learning for climate science.

Latest publications

2026

Hashmat Shadab Malik, Shahina Kunhimon, Muzammal Naseer, Fahad Khan, Salman Khan (2026) Hierarchical Self-supervised Adversarial Training for Robust Vision Models in Histopathology MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2025, PT I, p. 239-249 (Conference paper) Continue to DOI

2025

Abdelrahman Shaker, Syed Talal Wasim, Salman Khan, Juergen Gall, Fahad Khan (2025) GroupMamba: Efficient Group-Based Visual State Space Model 2025 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), p. 14912-14922 (Conference paper) Continue to DOI
Sagar Sone, Akshay Dudhane, Hiyam Debary, Mustansar Fiaz, Muhammad Akhtar Munir, Muhammad Sohail Danish, Paolo Fraccaro, Campbell D. Watson, Levente J. Klein, Fahad Khan, Salman Khan (2025) EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues 2025 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), p. 14303-14313 (Conference paper) Continue to DOI
Shehan Munasinghe, Hanan Gani, Wenqi Zhu, Jiale Cao, Eric Xing, Fahad Khan, Salman Khan (2025) VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos 2025 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), p. 19036-19046 (Conference paper) Continue to DOI
Johan Edstedt, Andre Mateus, Alberto Jaenal (2025) ColabSfM: Collaborative Structure-from-Motion by Point Cloud Registration 2025 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), p. 6573-6583 (Conference paper) Continue to DOI

Staff at the Computer Vision and Learning Systems

About the Department