Victoriya KASHTANOVA

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Fast real-time 3D modeling of objects and scenes (for Lay3rs.io)

KASHTANOVA Victoriya

Development of NeRF- and Diffusion- model based solutions for fast real-time 3D modeling of objects and scenes from video and image databases.

  • Computer Vision, Deep Learning
  • 3D scene and surface modelling
  • Models : 3DGS-, Nerf- and Diffusion- based models
  • Python, Pytorch, Docker, NerfStudio, SDFStudio, Unity, VR, Azure

3D Demo Video 1 3D Demo Video 2 3D Mesh Video 1

Post-graduate projects

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Physics-Based Deep Learning applied to Cardiology (Thesis)

KASHTANOVA Victoriya

Thesis : Learning Cardiac Electrophysiological Dynamics with PDE-based Physiological Constraints for Data-Driven Personalized Predictions in Cardiology, under supervision of Maxime Sermesant (Inria, Epione) and Patrick Gallinari (Sorbonne University, LIP6, MLIA).

  • Computer Vision, Physics-Based Deep Learning
  • Personalised predictions of Cardiac Electrophysiology dynamics
  • Development of 2 novel DL frameworks : APHYN-EP and EP-Net-2.0
  • Models : ResNet, U-Net, ViT etc.
  • Python, Pytorch

Thesis Code APHYN-EP Code EP-Net-2.0
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Unsupervised 3D Semantic Segmentation and Domain Adaptation (CrossMoDA challenge for MICCAI 2021 Conf.)

LY Buntheng, KASHTANOVA Victoriya, YANG Yingyu, MAILLOT Aurelien, NUNEZ-GARSIA Marta and SERMESANT Maxime

6th place in the Cross-Modality Domain Adaptation challenge, the main goal of which was to achieve unsupervised 3D multi-class vestibular schwannoma and cochlea segmentation. Results were published in the Medical Image Analysis journal.

  • Computer Vision, 2D/3D data generation, 3D/2.5D Segmentation, Deep Learning
  • Unsupervised 3D multi-class segmentation
  • Models : CycleGAN, 3/2.5 U-Net etc.
  • Python, Pytorch

Challenge Paper

Undergraduate projects

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True face detection (for Ubble.ai)

CLIVIO Oscar, CORVISIER Jean-Christophe, KASHTANOVA Victoriya and LOISEAU Romain

Solution development for real user authentification against various types of digital fraud, like : high-resolution photos or videos, 3D masks etc.

  • Image processing
  • Methods : defocusing, landmark analysis, eulerian magnification etc.
  • Python, OpenCV, Dlib

Code Poster
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CycleGAN analysis (Research project for DL course)

KASHTANOVA Victoriya, PESQUEREL Fabien

Deep investigation of the CycleGAN model paper : construction, training configurations, used metrics etc. Also studying the impact of the "anchor loss" (we added one matched pair of images and its loss to the unpaired datasets) on the model performance and the ability of the CycleGAN to do a transfer learning.

  • Computer Vision, Deep Learning
  • Models : GANs for Image-to-Image translation
  • Python, Pytorch, Google Cloud Platform

Code
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Deep Reinforcement Learning agents playing DOOM (Research project for RL and Computer Vision courses)

HURAULT Samuel and KASHTANOVA Victoriya

Investigation and optimization of two deep Reinforcement Learning frameworks which learn sensorimotor control in partially observable 3D environments.

  • Computer Vision, Deep RL
  • Methods : Direct Future Prediction (DFP), Deep Recurrent Q-Network (Arnold)
  • 3D environments : vizdoom, CARLA
  • Python, Pytorch, Google Cloud Platform

Results
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Computer vision for serial numbers (for Pzartech.com)

BOZOU Sacha, DE SOUSA Philippe, DUMONT Louis and KASHTANOVA Victoriya

Solution development for serial number recognition on the mechanical parts.

  • Image processing
  • Methods : reconstruction morphologique, binarisation, variable illumination filters
  • Super resolution
  • Python, OpenCV

Code Demo
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Automatic machine translation (Research project in mathematics)

FERREIRA DE SOUSA Philippe, KASHTANOVA Victoriya and SOUSSI Nada, supervised by MARLET Renaud (IMAGINE Lab)

Unsupervised alignment of bitexts.

  • Text processing
  • Methods : IBM Models, Word2vec
  • Python, C++

Code Demo Poster
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Population based epidemiological modelling (Research project in mathematics)

KASHTANOVA Victoriya, supervised by KABANIKHIN Sergey and KRIVOROTKO Olga

Knowing that the models of the epidemic spread could be described by systems of nonlinear ODEs whose coefficients characterize the features of population and disease.I worked on the construction and investigation of numerical algorithms for determining these coefficients by the global minimization of the multidimensional target function.

  • Unconstrained mathematical optimization
  • Development of tools to solve direct and inverse problems for ODEs systems
  • Methods : Gradient descent, Tensor Train, Simulated annealing etc.
  • Python, C++

Code Paper