Multi-modal modelling with multi-module mechanics
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Multi-modal modelling with multi-module mechanics autonomy in acomputational model of language learning by M.W Powers

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Published by Tilburg University in Tilburg .
Written in English

Book details:

Edition Notes

StatementDavid M.W. Powers.
ContributionsTilburg University Institute for Language Technology andArtificial Intelligence.
ID Numbers
Open LibraryOL20485958M

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Visual question answering (VQA) is a multi-modal task involving natural language processing (NLP) and computer vision (CV), which requires models to understand of both visual information and textual information simultaneously to predict the correct answer for the input visual image and textual question, and has been widely used in smart and intelligent transport systems, smart city, and other. Recently, multi-modal learning tasks such as image captioning [1,2], image-text matching [3,4,5], and visual question answering (VQA) [], which involve natural language processing and computer vision, have attracted considerable attention of researchers in these two ed with other multi-modal learning tasks, VQA is more difficult, since it requires the model to understand visual.   With the rapid development of Internet and multimedia services in the past decade, a huge amount of user-generated and service provider-generated multimedia data become available. These data are heterogeneous and multi-modal in nature, imposing great challenges for processing and analyzing them. Multi-modal data consist of a mixture of various types of data from different modalities such as. Acknowledgment. This work was supported in part by National Natural Science Foundation of China (Grant Nos. , U), in part funded by National Natural Science Foundation of China (Grant Nos. /DFG TRR, , ), and Suzhou Special Program (Grand No. SZ).