ENGThe Machine Learning Lab
Our R&D projects and publications
News
| Aug 7, 2020 | The work of Yaroslava Lochman, Oles Dobosevych, Rostyslav Hryniv, and James Pritts got accepted for Oral Presentation at WACV 2021. Paper and code coming soon. |
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| Jun 14, 2020 | Mariia Dobko, Ostap Viniavskyi, and Oles Dobosevych took the 3rd place at CVPR 2020 LID Challenge. See the paper NoPeopleAllowed: The Three-Step Approach to Weakly Supervised Semantic Segmentation and recorded presentation. |
| May 4, 2020 | The work of James Pritts, Zuzana Kukelova, Viktor Larsson, Yaroslava Lochman, and Ondrej Chum was published in TPAMI 2020. See the paper Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations and code. |
Projects
Weakly supervised semantic segmentation
To create models which can effectively segment images, the immense datasets of labeled data are required. It is often very time consuming. When labeling medical data an expert has to spend a lot of time and use his expertise which is very expensive.In order to decrease the resources spent on labeling while preserving the quality of the models, we develop a weakly-supervised approach for segmentation of different types of images. We test our method and show results on several distant datasets, including regular real-world objects (such as cars, planes, trees etc.), and on medical data (chest X-rays). We develop a solution which is capable to segment objects with a limited supervision.
ModelicaGym Toolbox for Applying Reinforcement Learning to Modelica Models
ModelicaGym toolbox was developed to employ Reinforcement Learning (RL) for solving optimization and control tasks in Modelica models. The developed tool allows connecting models using Functional Mock-up Interface (FMI) to OpenAI Gym toolkit in order to exploit Modelica equation-based modeling and co-simulation together with RL algorithms as a functionality of the tools correspondingly. Thus, ModelicaGym facilitates fast and convenient development of RL algorithms and their comparison when solving optimal control problem for Modelica dynamic models. The toolbox functionality validation is performed on Cart-Pole balancing problem.Authors: Oleh Lukianykhin, Tetiana Bogodorova
Robust Methods for Single View Geometry Estimation
The project focuses on robust methods for camera auto-calibration, estimating scene plane rectification and symmetry detection. These methods are useful for important vision tasks like visual localization and 3D reconstruction.Team:
- James Pritts (Facebook Reality Labs, Supervisor)
- Yaroslava Lochman (UCU)
- Kostiantyn Liepieshov (UCU)
- Oles Dobosevych (UCU)
- Rostyslav Hryniv (UCU)
Face recognition system
Користувач реєструється з телефону і робить одну фотографію. Після цього на основі системи розпізнавання облич і побудови векторних представлень, будується представлення обличчя. Тепер коли клієнт, який зареєструвався в системі прийде в магазин, він буде розпізнаний і продавцю в магазині прийде інформація про вподобання клієнта, щоб той міг порадити йому кращі пропозиції.Автори: Олесь Добосевич, Матвій Ковтун, Олег Смолкін, Антон Тарасов, Михайло Іванків
Низьковартісна система для запису пробивних параметрів преса
Із метою здешевлення процесів виробництва на одному із львівських заводів ми розробили IoT рішення, для автоматичного підрахунку кількості загальних ударів пресу, а також подрібнення даних для окремих форм із мінімальним втручанням оператора.Автори: Дзвенимира Яріш, Богдан Петришак, Юрій Лаба, Олександр Пригода, Юрій Стасінчук
Publications
J. Pritts, Z. Kukelova, V. Larsson, Y. Lochman, O. Chum. Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2020
Publication
J. Pritts, Z. Kukelova, V. Larsson, Y. Lochman, O. Chum. Minimal Solvers for Rectifying from Radially-Distorted Scales and Change of Scales. International Journal of Computer Vision (IJCV) 2020
O. Kupyn, D. Pranchuk. Fast and Efficient Model for Real-Time Tiger Detection In The Wild. arXiv preprint, 2019
Publication
O. Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang. DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better. arXiv preprint, accepted for ICCV 2019
Publication
Irynei Baran, O. Kupyn, A. Kravchenko. Safe Augmentation: Learning Task-Specific Transformations from Data. arXiv preprint, accepted for WACV 2020
Publication
O. Lukianykhin, T. Bogodorova. ModelicaGym: Applying Reinforcement Learning to Modelica Models. arXiv preprint, accepted for publication in EOOLT 2019
Publication
O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, J. Matas. DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks, IEEE CVPR, Salt Lake City, USA, June 2018
Publication, Poster
M. Mykhailych. Application of Generative Neural Models for Style Transfer Learning in Fashion, Master Thesis, 2018
Publication
A. Stehnii. Generation of code from text description with syntactic parsing and Tree2Tree model, Master Thesis, 2018
Publication