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...
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dc.contributor.authorBakshandaeva, D.D.-
dc.contributor.authorDimitrov, D.V.-
dc.contributor.authorArkhipkin, V.S.-
dc.contributor.authorShonenkov, A.V.-
dc.contributor.authorPotanin, M.S.-
dc.contributor.authorKarachev, D.K.-
dc.contributor.authorKuznetsov, A.V.-
dc.contributor.authorVoronov, A.D.-
dc.contributor.authorPetiushko, A.A.-
dc.contributor.authorDavydova, V.F.-
dc.contributor.authorTutubalina, E.V.-
dc.date.accessioned2023-02-21 10:17:15-
dc.date.available2023-02-21 10:17:15-
dc.date.issued2023-02-
dc.identifierDspace\SGAU\20230216\102049ru
dc.identifier.citationBakshandaeva 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.uri10.18287/2412-6179-CO-1220-
dc.identifier.urihttp://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Many-heads-but-one-brain-FusionBrain-–-a-single-multimodal-multitask-architecture-and-a-competition-102049-
dc.description.abstractSupporting 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.sponsorshipWe 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.isoenru
dc.publisherСамарский национальный исследовательский университетru
dc.relation.ispartofseries47;1-
dc.subjectmultimodality, multitask, bilinguality, foundation models, FusionBrain challengeru
dc.titleMany heads but one brain: FusionBrain – a single multimodal multitask architecture and a competitionru
dc.typeArticleru
dc.textpart01), 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...-
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