Papers by Alexander Kapitanov
RusCode: Russian Cultural Code Benchmark for Text-to-Image Generation (2025.findings-naacl)
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Viacheslav Vasilev, Julia Agafonova, Nikolai Gerasimenko, Alexander Kapitanov, Polina Mikhailova, Evelina Mironova, Denis Dimitrov
| Challenge: | Text-to-image generation models exhibit a strong bias toward English-speaking cultures, ignoring or misrepresenting the unique characteristics of other language groups, countries, and nationalities. |
| Approach: | They propose a RusCode benchmark to evaluate the quality of text-to-image generation containing elements of the Russian cultural code. |
| Outcome: | The proposed model is based on 1250 text prompts in Russian and their translations into English. |
Logos as a Well-Tempered Pre-train for Sign Language Recognition (2025.emnlp-main)
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| Challenge: | Existing data on sign languages is limited, resulting in ambiguity in dataset labeling . similar signs can have different semantic meanings, which leads to ambiguous labeling. |
| Approach: | They propose to use a Russian sign language dataset as a universal encoder for other languages . they propose to explicitly annotate visually similar signs to improve model quality . |
| Outcome: | The proposed model outperforms current state-of-the-art models and gets competitive results for the AUTSL dataset. |
Multimodal Evaluation of Russian-language Architectures (2026.eacl-long)
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Artem Chervyakov, Ulyana Isaeva, Anton Emelyanov, Artem Safin, Maria Tikhonova, Alexander Kharitonov, Yulia Lyakh, Petr Surovtsev, Denis Shevelev, Vildan Saburov, Vasily Konovalov, Elisei Rykov, Ivan Sviridov, Amina Miftakhova, Ilseyar Alimova, Alexander Panchenko, Alexander Kapitanov, Alena Fenogenova
| Challenge: | Multimodal large language models (MLLMs) are at the center of research attention, yet intelligence, limitations, and risks remain insufficiently understood. |
| Approach: | They propose an open multimodal evaluation framework for Russian-spoken architectures . the framework is instruction-based and includes 18 newly constructed evaluation tasks . |
| Outcome: | The proposed framework provides a replicable methodology for constructing multimodal benchmarks in Russian-spoken architectures. |