Dmytro Mishkin
Computer vision and deep learning consultant, PhD
I save your time and money by advising directions that are more suitable, from a performance, sustainability, and business perspective. While the current frameworks allow good software engineers to replace domain experts in computer vision, when it comes to implementation, expertise is required to decide, which should and, more importantly, should not be implemented.
I currently do not take projects
Recent consulting projects
- realtime object tracking for augmented reality
- camera localization for virtual reality
- deep genomics
- anomaly detection for material quality inspection
- image recommendation for fashion
Serve as volunteer Member of the Expert Committee on Artificial Intelligence at Ministry of Digital Transformation Of Ukraine. Areas of responsibility: science and education.
Author of the official Glossary of the Terms in the field of Artificial Intelligence (in Ukrainian).
Co-founder of the Eastern European Computer Vision Conference. We running the largest computer vision conference in Eastern Europe to connect industrial and academic worlds.
Have started Ukrainian Research Group "Szkocka" is an initiative to promote and advance Ukrainian science. It is a platform for cooperation between researchers and supervisors on doing high-quality academic research. It is non-government organization free from bureaucracy and regulations, it exists due to free cooperation and donations.
We supervise and fund students, PhD students and volunteers research in computer vision and machine learning area. Group is named after Lviv mathematician community in 1930s
I did my PhD at CTU in Prague under supervision of Prof. Jiri Matas to deepen and expand my expertise in computer vision and machine learning. My research is mostly devoted to wide baseline stereo and local features: the workhorse of 3D reconstuction, SLAM and image retrieval. During the study, I have done reserch internship at Intel Labs Munich, where I studied classical and learning-based navigation algorithms.
My PhD thesis "Learning and Crafting for the Wide Multiple Baseline Stereo got Dean's Prize, Rector's prize and got into the final of Antonín Svoboda Award for the Best Ph.D. Thesis.
I am the maintainer of the open source Kornia library — OpenCV in PyTorch.
Co-founder and CTO of Clear Research. Team under my supervision have developed a mobile visual commerce system for madora.co app. In particular:
- bags and shoes recommendation engine, based on actual photos of things user like, or already have;
- proprietary deep learning powered similarity search engine, based on user tap on camera photo;
- proprietary algorithm, which discovers potential items to sell, based on photo and description from supplier web-page;
- proprietary automatic image adjustment algorithm, so all things we sell, have standardized view and and the photos are of desired quality, even the items are from different suppliers
Worked as visiting researcher at Center of Machine perception at CTU in Prague. I have developed MODS - the state-of-the-art method for the wide baseline stereo matching under the extreme viewpoint change.
Was an Assistant Professor at National Technical University of Ukraine "KPI". I taught masters and undergraduate courses:
- Image recognition
- Satellite imagery processing
- Microcontroller systems
Invited talks
Talk "What is the state-of-the-art in 2025 and how can we be sure?"
Talk "How to move the goalposts: coming up with yet unsolved challenges for SfM"
Talk "Affine Correspondences and Where to Find Them"
Keynote "Crafting and Learning for Image Matching"
Talk "Benchmarking Robust Estimation Methods"
Talk "Local features: from paper to practice".
"Crafting and learning for image matching".
"Deep-learned vs Handcrafted navigation".
"Crafting and learning for image matching".
Convolutional neural networks from basics to the recent advances
Interviews
- 24.01.2020 [Sayak Paul Interviews]
- 12.04.2020 [Chai Time Data Science]
- 08.09.2017 [The Ukrainians]
Selected publications
Teaching
Computer Vision Methods
Lectures on correspondence methods for computer vision: local feature detectors, descriptors and matching. [MPV course] at CTU in Prague. Slides and videos are available at the course page.
Efficient ML Research
Lecture on How to navigate through the ML research information flood. For Ukrainian Catholic University Winter Schhol on "How to run an effective machine learning research 2022"
Computer Vision Methods
Lectures on correspondence methods for computer vision: local feature detectors, descriptors and matching. [MPV course] at CTU in Prague. Slides and videos are available at the course page.
Computer Vision Methods
Lectures on correspondence methods for computer vision: local feature detectors, descriptors and matching. [MPV course] at CTU in Prague. Slides and videos are available at the course page.
Local features in computer vision
Private 3 days workshop about modern local detectors, descriptor for correspondence search.
For University of Ostrava Institute for Research and Applications of Fuzzy Modeling
Computer Vision Methods
I have re-designed and taught the practical part of the [MPV course] at CTU in Prague.
Visual object tracking course
For Winter School at Ukrainian Catholic University. [Course link]
Intro into Deep Learning course
Community duty
I serve as a reviewer to the major computer vision -- CVPR, ICCV, ECCV, 3DV and some machine learning conferences, such as NeurIPS and ICLR. Outstanding Reviewer for CVPR 2021, 2023.Contact
The best way to contact me is email: ducha.aiki@gmail.comI also write about computer vision and science on:
Mastodon
my new blog
medium (old blog)
blog devoted to wide baseline stereo.
In my free time I am playing kaggle (Kaggle Master), chess and practicing aikido. Yes, that's where my nickname comes from.