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        <title>Multi-grained Attention with Object-level Grounding for Visual Question Answering</title>
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        <namePart type="given">Pingping</namePart>
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        <namePart type="given">Jianhui</namePart>
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    <name type="personal">
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    <abstract>Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question. Most approaches train attention models from a coarse-grained association between sentences and images, which tends to fail on small objects or uncommon concepts. To address this problem, this paper proposes a multi-grained attention method. It learns explicit word-object correspondence by two types of word-level attention complementary to the sentence-image association. Evaluated on the VQA benchmark, the multi-grained attention model achieves competitive performance with state-of-the-art models. And the visualized attention maps demonstrate that addition of object-level groundings leads to a better understanding of the images and locates the attended objects more precisely.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1349</identifier>
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%0 Conference Proceedings
%T Multi-grained Attention with Object-level Grounding for Visual Question Answering
%A Huang, Pingping
%A Huang, Jianhui
%A Guo, Yuqing
%A Qiao, Min
%A Zhu, Yong
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluí s.
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F huang-etal-2019-multi-grained
%X Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question. Most approaches train attention models from a coarse-grained association between sentences and images, which tends to fail on small objects or uncommon concepts. To address this problem, this paper proposes a multi-grained attention method. It learns explicit word-object correspondence by two types of word-level attention complementary to the sentence-image association. Evaluated on the VQA benchmark, the multi-grained attention model achieves competitive performance with state-of-the-art models. And the visualized attention maps demonstrate that addition of object-level groundings leads to a better understanding of the images and locates the attended objects more precisely.
%R 10.18653/v1/P19-1349
%U https://aclanthology.org/P19-1349/
%U https://doi.org/10.18653/v1/P19-1349
%P 3595-3600

Markdown (Informal)

[Multi-grained Attention with Object-level Grounding for Visual Question Answering](https://aclanthology.org/P19-1349/) (Huang et al., ACL 2019)

• Multi-grained Attention with Object-level Grounding for Visual Question Answering (Huang et al., ACL 2019)

ACL

• Pingping Huang, Jianhui Huang, Yuqing Guo, Min Qiao, and Yong Zhu. 2019. Multi-grained Attention with Object-level Grounding for Visual Question Answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3595–3600, Florence, Italy. Association for Computational Linguistics.