Research
研究
研究業績
カテゴリー
研究領域
年
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2024年度 研究報告ユビキタスコンピューティングシステム,優秀論文賞（UBI）：Vision Transformerから畳み込みニューラルネットワーク への知識蒸留手法の提案
前羽 利治，河野 慎，松尾 豊
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Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model
Xin Zhang*, Jiaxian Guo*, Paul Yoo, Yutaka Matsuo, Yusuke Iwasawa
2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)
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HAWK-Net: Hierarchical Attention Weighted Top-K Network for Megapixel Image Classification
Hitoshi Nakanishi, Masahiro Suzuki, Yutaka Matsuo
IPSJ, (2023).
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顔の角度情報を用いたDeepFake動画の検出手法の提案
蔭山智, 鈴木雅大, 落合桂一, 松尾豊
電子情報通信学会和文論文誌D, (2023).
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Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model
Xin Zhang, Jiaxian Guo, Paul Yoo, Yutaka Matsuo, Yusuke Iwasawa
AI4CC workshop of The IEEE/CVF Conference on Computer Vision and Pattern Recognition
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Robustifying Vision Transformer Without Retraining From Scratch Using Attention Based Test-Time Adaptation
Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo
New Generation Computing, (2022).[paper]
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Fixing the train-test objective discrepancy: Iterative Image Inpainting for Unsupervised Anomaly Detection
Hitoshi Nakanishi, Masahiro Suzuki, Yutaka Matuo.
J-Stage in August Vol.30, (2022).
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Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment
Takeshi Kojima, Yutaka Matsuo, and Yusuke Iwasawa.
the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022)
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第25回画像の認識・理解シンポジウム (MIRU2022) MIRU優秀賞: Pixel vs. Object: 変化キャプショニングにおける最適な画像表現についての研究
土居健人, 濱口竜平, 岩澤有祐, 大西正輝, 松尾豊, 櫻田健,
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Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization
Yusuke Iwasawa, and Yutaka Matsuo.
Advances in Neural Information Processing Systems 2021 (NeurIPS2021, Spotlight). December 2021.