Milan Šulc, Ph.D.

Head of Rossum AI Labs, interested in Computer Vision & Machine Learning
[LinkedIn] milan.sulc@rossum.ai

Publications

L. Picek, M.Šulc, Y. Patel, J.Matas. Plant Recognition by AI: Deep Neural Nets, Transformers and kNN in Deep Embeddings. [open access] [PDF]
Frontiers in Plant Science (IF~6.6) 2022.

M. Skalický, Š. Šimsa, M. Uřičář, M. Šulc. Business Document Information Extraction: Towards Practical Benchmarks. [Springer Link] [arXiv] [PDF]
CLEF 2022. Lecture Notes in Computer Science, vol 13390.

A. Joly, H. Goëau, S. Kahl, L. Picek, T. Lorieul, E. Cole, B. Deneu, M. Servajean, A. Durso, H. Glotin, R. Planqué, W. P. Vellinga, A. Navine, H. Klinck, T. Denton, T. Eggel, P. Bonnet, M. Šulc, M. Hruz, H. Müller. Overview of LifeCLEF 2022: an evaluation of Machine-Learning based Species Identification and Species Distribution Prediction. [Springer Link] [PDF]
CLEF 2022. Lecture Notes in Computer Science, vol 13390.

L. Picek, M. Šulc, J. Heilmann-Clausen, J. Matas. Overview of FungiCLEF 2022: Fungi recognition as an open set classification problem. [PDF] [web]
In Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, 2022.

L. Picek, M. Šulc, J. Matas, J. Heilmann-Clausen, T. S. Jeppesen and E. Lind. Automatic Fungi Recognition: Deep Learning Meets Mycology. [pdf] [open access]
Sensors (IF~3.6) 2022, 22, 633.

A. Joly, H. Goëau, S. Kahl, L. Picek, T. Lorieul, E. Cole, B. Deneu, M. Servajean, A. Durso, I. Bolon, H. Glotin, R. Planqué, W. P. Vellinga, H. Klinck, T. Denton, I. Eggel, P. Bonnet, H. Müller, M. Šulc. LifeCLEF 2022 Teaser: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction. [Springer Link] [PDF]
European Conference on Information Retrieval, 2022.

T. Šipka, M. Šulc and J. Matas. The Hitchhiker’s Guide to Prior-Shift Adaptation. [PDF] [arXiv] [github]
The IEEE/CVF Winter Conference on Applications of Computer Vision, 2022.

L. Picek, M. Šulc, J. Matas, J. Heilmann-Clausen, T. S. Jeppesen, T. Læssøe, T. Frøslev. Danish Fungi 2020 – Not Just Another Image Recognition Dataset. [PDF] [arXiv] [dataset]
The IEEE/CVF Winter Conference on Applications of Computer Vision, 2022.

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

M. Šulc, 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. Šulc 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. Šulc 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. Šulc 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.

PhD Thesis

M. Šulc. Fine-grained Recognition of Plants and Fungi from Images. [PDF]
Ph.D. thesis at the Dept. of Cybernetics, Faculty of Electrical Engineering, Czech Technical Unviersity in Prague, 2021.

Supervised Theses

M. Skalický. Relative Layout Matching for Document Data Extraction. [PDF] [dspace]
Master thesis, Czech Technical Unviersity in Prague, 2022. Related student’s publication: [CLEF 2022 paper]

T. Šipka. Adaptation of CNN Classifiers to Prior Shift. [PDF] [dspace]
Master thesis, Czech Technical Unviersity in Prague, 2021. Related student’s publication: [WACV 2022 paper]

E. Babayeva. Learning segmentation from multiple datasets with different label sets. [PDF] [dspace]
Master thesis, Czech Technical Unviersity in Prague, 2019.