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Milan Šulc

Researcher
Computer Vision & Machine Learning

Milan.Sulc@cvut.cz

[Resume] [PhD thesis]

Ing. et Ing. Milan Šulc, Ph.D.,
Katedra kybernetiky FEL CVUT,
Karlovo namesti 13,
121 35 Prague, Czech Republic

Office: G2 (building G, room 2), Map

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


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.


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.

Pattern Recognition and Machine Learning (B4B33RPZ, BE5B33RPZ)

All course materials are available on the corresponding CourseWare page, incuding the lab materials.

Computer Vision Methods (A4M33MPV, AE4M33MPV)

All course materials are available on the corresponding CourseWare page, including the lab materials.