| 研究課題/領域番号 |
20K19822
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| 研究機関 | 大阪大学 |
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研究代表者 |
GARCIA・DOCAMPO NOA 大阪大学, データビリティフロンティア機構, 特任研究員(常勤) (80870005)
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| 研究期間 (年度) |
2020-04-01 – 2022-03-31
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| キーワード | art analysis / visual understanding / deep neural networks / transfer learning / question answering |
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| 研究実績の概要 |
During the first year of research, we collected a dataset for visual question answering on art. The dataset was presented an international workshop of computer vision for art (VisArt) in one of the most important computer vision conferences (ICCV) in front of worldwide experts. Additionally, we conducted extensive experiments on the dataset and presented a new model to address visual question answering on art, obtaining outstanding performance.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We already collected the dataset for visual question answering in art and performed extensive evaluation of different baselines and models. We also applied these techniques to automatic description of artworks and visual question answering for natural images.
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| 今後の研究の推進方策 |
For future research, we plan keep exploring better models for high-level representation of art and apply them to visual question answering, image description generation and semi-supervised learning. We are currently studying emotions associated to art using image representations.
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| 次年度使用額が生じた理由 |
Because of COVID-19 travel restrictions, I could not use the funding as planned to attend conferences and give talks about our research.
For next year, I will use the funding on: 1) traveling to international conferences, 2) paying journal publication fees, 3) dataset annotations, and 4) GPU machine purchase.
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