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KO11003001-20230304-0025.pdf
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Download
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:987.6 KB
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:Feb 20, 2024 |
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Total downloads since Feb 20, 2024 : 32
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| Title |
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Prototypical Contrastive Transfer Learning for multimodal language understanding
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小槻, 誠太郎
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オツキ, セイタロウ
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Otsuki, Seitaro
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| Affiliation |
慶應義塾大学理工学部情報工学科
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Department of Information and Computer Science, Keio University
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慶應義塾大学AI・高度プログラミングコンソーシアム
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ケイオウ ギジュク ダイガク AI・コウド プログラミング コンソーシアム
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Keiō gijuku daigaku AI kōdo puroguramingu konsōshiamu
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2023
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AICカンファレンス予稿集
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2023
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25
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25
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We focus on the task of identifying target objects in domestic environments according to free-form natural language instructions. In this work, we propose a novel transfer learning approach for multimodal language understanding, Prototypical Contrastive Transfer Learning (PCTL) which uses a new contrastive loss, Dual ProtoNCE. We introduce PCTL to the target task. To validate PCTL, we built new real-world and simulation datasets. Our experiment demonstrated that PCTL outperformed existing methods. Specifically, PCTL achieved an accuracy of 78.1%, while simple fine-tuning achieved an accuracy of 73.4%.
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| Multimodal Language Understanding
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| Prototypical Contrastive Transfer Learning
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会議名 : AICカンファレンス2023
開催地 : 慶應義塾大学日吉キャンパス
日時 : 2023年3月4日
第2章ポスター発表要旨
ポスター要旨-2
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