Måns Larsson

According to our database, Måns Larsson authored at least 11 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models.

Muhammad Usman Akbar, Måns Larsson, Anders Eklund

CoRR, 2023

2022

Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?

Måns Larsson, Muhammad Usman Akbar, Anders Eklund

CoRR, 2022

2021

Back to the Feature: Learning Robust Camera Localization From Pixels To Pose.

Paul-Edouard Sarlin, Ajaykumar Unagar, Måns Larsson, Hugo Germain, Carl Toft, Viktor Larsson, Marc Pollefeys, Vincent Lepetit, Lars Hammarstrand, Fredrik Kahl, Torsten Sattler

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2019

Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization.

Måns Larsson, Erik Stenborg, Carl Toft, Lars Hammarstrand, Torsten Sattler, Fredrik Kahl

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

A Cross-Season Correspondence Dataset for Robust Semantic Segmentation.

Måns Larsson, Erik Stenborg, Lars Hammarstrand, Marc Pollefeys, Torsten Sattler, Fredrik Kahl

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction.

Anurag Arnab, Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Måns Larsson, Alexander Kirillov, Bogdan Savchynskyy, Carsten Rother, Fredrik Kahl, Philip H. S. Torr

IEEE Signal Process. Mag., 2018

Revisiting Deep Structured Models for Pixel-Level Labeling with Gradient-Based Inference.

Måns Larsson, Anurag Arnab, Shuai Zheng, Philip H. S. Torr, Fredrik Kahl

SIAM J. Imaging Sci., 2018

Robust abdominal organ segmentation using regional convolutional neural networks.

Måns Larsson, Yuhang Zhang, Fredrik Kahl

Appl. Soft Comput., 2018

2017

Learning Arbitrary Potentials in CRFs with Gradient Descent.

Måns Larsson, Fredrik Kahl, Shuai Zheng, Anurag Arnab, Philip H. S. Torr, Richard I. Hartley

CoRR, 2017

Max-Margin Learning of Deep Structured Models for Semantic Segmentation.

Måns Larsson, Jennifer Alvén, Fredrik Kahl

Proceedings of the Image Analysis - 20th Scandinavian Conference, 2017

A Projected Gradient Descent Method for CRF Inference Allowing End-to-End Training of Arbitrary Pairwise Potentials.

Måns Larsson, Anurag Arnab, Fredrik Kahl, Shuai Zheng, Philip H. S. Torr

Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2017