Отрывок: 01), the final result can range from 0 to 4. We also measured the performance of state-of-the-art single-task models – PLBART [52], Easter2 [53], MDETR [48] – for each of the subtasks on our private test sets (see Table 4). It should be noted that the vast majority of models (including the state-of-the-art one) s...
Полная запись метаданных
Поле DC | Значение | Язык |
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dc.contributor.author | Bakshandaeva, D.D. | - |
dc.contributor.author | Dimitrov, D.V. | - |
dc.contributor.author | Arkhipkin, V.S. | - |
dc.contributor.author | Shonenkov, A.V. | - |
dc.contributor.author | Potanin, M.S. | - |
dc.contributor.author | Karachev, D.K. | - |
dc.contributor.author | Kuznetsov, A.V. | - |
dc.contributor.author | Voronov, A.D. | - |
dc.contributor.author | Petiushko, A.A. | - |
dc.contributor.author | Davydova, V.F. | - |
dc.contributor.author | Tutubalina, E.V. | - |
dc.date.accessioned | 2023-02-21 10:17:15 | - |
dc.date.available | 2023-02-21 10:17:15 | - |
dc.date.issued | 2023-02 | - |
dc.identifier | Dspace\SGAU\20230216\102049 | ru |
dc.identifier.citation | Bakshandaeva D, Dimitrov D, Arkhipkin V, Shonenkov A, Potanin M, Karachev D, Kuznetsov A, Voronov A, Petiushko A, Davydova V, Tutubalina E. Many heads but one brain: FusionBrain – a single multimodal multitask architecture and a competition. Computer Optics 2023; 47(1): 185-195. DOI: 10.18287/ 2412-6179-CO-1220. | ru |
dc.identifier.uri | 10.18287/2412-6179-CO-1220 | - |
dc.identifier.uri | http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Many-heads-but-one-brain-FusionBrain-–-a-single-multimodal-multitask-architecture-and-a-competition-102049 | - |
dc.description.abstract | Supporting the current trend in the AI community, we present the AI Journey 2021 Challenge called FusionBrain, the first competition which is targeted to make a universal architecture which could process different modalities (in this case, images, texts, and code) and solve multiple tasks for vision and language. The FusionBrain Challenge combines the following specific tasks: Code2code Translation, Handwritten Text recognition, Zero-shot Object Detection, and Visual Question Answering. We have created datasets for each task to test the participants’ submissions on it. Moreover, we have collected and made publicly available a new handwritten dataset in both English and Russian, which consists of 94,128 pairs of images and texts. We also propose a multimodal and multitask architecture – a baseline solution, in the centre of which is a frozen foundation model and which has been trained in Fusion mode along with Single-task mode. The proposed Fusion approach proves to be competitive and more energy-efficient compared to the task-specific one. | ru |
dc.description.sponsorship | We would like to thank Sber and SberCloud for granting the GPU-resources to us to experiment with different architectures and also to the participants to train their models, and for supporting the FusionBrain Challenge in general. | ru |
dc.language.iso | en | ru |
dc.publisher | Самарский национальный исследовательский университет | ru |
dc.relation.ispartofseries | 47;1 | - |
dc.subject | multimodality, multitask, bilinguality, foundation models, FusionBrain challenge | ru |
dc.title | Many heads but one brain: FusionBrain – a single multimodal multitask architecture and a competition | ru |
dc.type | Article | ru |
dc.textpart | 01), the final result can range from 0 to 4. We also measured the performance of state-of-the-art single-task models – PLBART [52], Easter2 [53], MDETR [48] – for each of the subtasks on our private test sets (see Table 4). It should be noted that the vast majority of models (including the state-of-the-art one) s... | - |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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21_Bakshandaeva_Dimitrov_Arkhipkin_Shonenkov_Potanin_Karachev_Kuznetsov-aut-MA-L-JuN2-gr.pdf | Основная статья | 1.49 MB | Adobe PDF | Просмотреть/Открыть |
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