Milan Šulc, Ph.D.

Computer Vision & Machine Learning
[Resume] [LinkedIn] [PhD thesis]
milansulc01@gmail.com

Publications

T. Sipka, M. Sulc and J. Matas. The Hitchhiker’s Guide to Prior-Shift Adaptation. [arXiv] [github]
arXiv:2106.11695, 2021. Accepted to WACV 2022.

L. Picek, M. Sulc, J. Matas, J. Heilmann-Clausen, T. S. Jeppesen, T. Læssøe, T. Frøslev. Danish Fungi 2020 – Not Just Another Image Recognition Dataset. [arXiv] [web]
arXiv:2103.10107, 2021. Accepted to WACV 2022.

M. Sulc, L. Picek, J. Matas, T. S. Jeppesen, J. Heilmann-Clausen, Fungi Recognition: A Practical Use Case. [PDF] github
The IEEE Winter Conference on Applications of Computer Vision. 2020.

M. Sulc and J. Matas, Improving CNN classifiers by estimating test-time priors. [PDF] [github]
The IEEE International Conference on Computer Vision (ICCV) Workshops (TASK-CV 2019).

L. Picek, M. Šulc, J. Matas, Recognition of the Amazonian flora by inception networks with test-time class prior estimation. [PDF] [models & data] [tf-slim code]
In Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum, 2019.

M. Sulc, L. Picek and J. Matas, Plant Recognition by Inception Networks with Test-time Class Prior Estimation. [PDF] [models] [tf-slim code]
In Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum, 2018.

P. Bonnet, H. Goeau, S.T. Hang, M. Lasseck, M. Sulc, V. Malecot, P. Jauzein, J-C. Melet, Ch. You, A. Joly. Plant Identification: Experts vs. Machines in the Era of Deep Learning (Book Chapter). [Springer Link]
In A. Joly, S. Vrochidis, K. Karatzas, A. Karppinen, P. Bonney (Ed.) Multimedia Tools and Applications for Environmental & Biodiversity Informatics. 2018. ISBN: 978-3-319-76445-0.

M. Sulc and J. Matas, Fine-grained Recognition of Plants from Images. [PDF] [HTML] [tf-slim code]
Plants in Computer Vision [Special Issue], Plant Methods. 2017. ISSN: 1746-4811. Impact Factor 3.51

M. Sulc and J. Matas, Learning with Noisy and Trusted Labels for Fine-Grained Plant Recognition. [PDF] [tf-slim code]
In Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum, 2017.

M. Sulc, D. Mishkin and J. Matas, Very Deep Residual Networks with MaxOut for Plant Identification in the Wild. [PDF] [Presentation]
In Working Notes of CLEF 2016 - Conference and Labs of the Evaluation Forum, 2016. Oral presentation.

M. Sulc and J. Matas, Significance of Colors in Texture Datasets. [PDF]
In Proceedings of the 21st Computer Vision Winter Workshop, 2016. Oral presentation.

M. Sulc, A. Gordo, D. Larlus and F. Perronnin, System and Method for Product Identification. [Link]
US Patent No. 9,443,164. Issued in August 2016.

M. Sulc and J. Matas, Fast Features Invariant to Rotation and Scale of Texture. [Springer Link] [PDF] [code]
European Conference on Computer Vision (ECCV) 2014 Workshops (LBP’14). Springer International Publishing, 2014. Oral presentation.

M. Sulc and J. Matas, Texture-Based Leaf Identification. [Springer Link] [PDF] European Conference on Computer Vision (ECCV) 2014 Workshops (CVPPP’14). Springer International Publishing, 2014. Poster.

M. Sulc and J. Matas, Kernel-mapped Histograms of Multi-scale LBPs for Tree Bark Recognition. [PDF]
In Proceedings of the 28th Conference on Image and Vision Computing New Zealand, 2013. Oral presentation.