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October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.

 

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?

Benchmarking and Data Synthesis for Colorization of Manga Sequential Pages for Augmented Reality

P. 608–611.
Golyadkin M., Saraev S., Makarov I.

This paper introduces an innovative approach to manga colorization within augmented reality (AR) environments, focusing on the unique challenges posed by colorizing photos of manga books. We present a novel method using diffusion models to generate a synthetic dataset that accurately replicates photographed manga pages. Additionally, we have compiled a dataset of real manga photographs, capturing diverse environmental conditions. Integrating these datasets, we established a comprehensive benchmark to evaluate colorization models in scenarios that simulate AR applications. This benchmark was validated through a human study, confirming the accuracy of our metrics across both datasets. We also showed that domain adaptation may improve model performance. Paving the way for practical applications, our framework enables the creation of an AR application designed to execute manga colorization effectively.

Language: English
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Keywords: бенчмаркDomain adaptationДоменная адаптацияbenchmark dataset

In book

2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)
IEEE, 2024.
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