Research

Our research interest focuses on Multimedia Analysis and Artificial Intelligence. Examples of our research topics include image recognition, speech recognition and video retrieval. We are developing algorithms and systems on TSUBAME, the supercomputer at the Science Tokyo. We welcome researchers and students who are interested in learning frameworks such as deep learning and zero-shot/few-shot learning.

井上研究室では、マルチメディア情報処理の分野で、画像認識・音声認識・映像検索などの人工知能技術に関する研究を実施しています。 大規模データを処理するためのアルゴリズムやシステムの開発では、東京科学大学のスーパーコンピューターTSUBAMEを活用しています。 深層学習やゼロショット学習などの学習方式や、人工知能を構築するためのデータ作成に興味のある研究者・学生を募集中です。

Members

 - Nakamasa Inoue (Associate Professor, contact e-mail: inoue at c.titech.ac.jp)
 - Undergraduate students: 2, Graduate students: 9

 - 井上 中順 (准教授, e-mail: inoue at c.titech.ac.jp)
 - 学部生 2名，大学院生 9名

Publications

• Mao Tomita, Ikuro Sato, Rei Kawakami, Nakamasa Inoue, Satoshi Ikehata, Masayuki Tanaka, A Simple Fine-Tuning Strategy Based on Bias-Variance Ratios of Layer-Wise Gradients, Proc. Asian Conference on Computer Vision (ACCV), pp 192-209, 2024.
• Shun Iwase, Shuya Takahashi, Nakamasa Inoue, Rio Yokota, Ryo Nakamura, Hirokatsu Kataoka, Eisaku Maeda, On the Relationship Between Double Descent of CNNs and Shape/Texture Bias Under Learning Process, Proc. International Conference on Pattern Recognition (ICPR), pp 95-109, 2024.
• Mahiro Ukai, Shuhei Kurita, Atsushi Hashimoto, Yoshitaka Ushiku, Nakamasa Inoue, AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering, Proc. ACM Multimedia (ACMMM), pp. 9234-9243, 2024.
• Ruoyue Shen, Nakamasa Inoue, and Koichi Shinoda, Pyramid Coder: Hierarchical Code Generation for Compositional Visual Question Answering, Proc. IEEE International Conference on Image Processing (ICIP), 2024.
• Takumi Karasawa, Nakamasa Inoue, and Rei Kawakami, Spatiality-aware Prompt Tuning for Few-shot Small Object Detection, Proc. IEEE International Conference on Image Processing (ICIP), 2024. (Best Student Paper Award 2nd runner-up)
• Ryo Nakamura, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka, Scaling Backwards: Minimal Synthetic Pre-training, Proc. European Conference on Computer Vision (ECCV), 2024.
• Ryosuke Yamada, Kensho Hara, Hirokatsu Kataoka, Koshi Makihara, Nakamasa Inoue, Rio Yokota, Yutaka Satoh, Formula-Supervised Visual-Geometric Pre-training, Proc. European Conference on Computer Vision (ECCV), 2024.
• Go Ohtani, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka, Yoshimitsu Aoki, Rethinking Image Super Resolution from Training Data Perspectives, Proc. European Conference on Computer Vision (ECCV), 2024.
• Li Zhengxiao, Nakamasa Inoue, Locally Aligned Rectified Flow Model for Speech Enhancement Towards Single-Step Diffusion, Proc. Interspeech, 2024.
• Nakamasa Inoue, Shinta Otake, Takumi Hirose, Masanari Ohi, Rei Kawakami, ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks, IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP), 2024.
• Takeshi Kaneko, Rei Kawakami, Takeshi Naemura, Nakamasa Inoue, Augmenting Pass Prediction via Imitation Learning in Soccer Simulations, Proc. CVPR Workshop on Computer Vision in Sports (CVsports), 2024.
• Ryo Hayamizu, Shota Nakamura, Sora Takashima, Hirokatsu Kataoka, Ikuro Sato, Nakamasa Inoue, Rio Yokota, SIFTer: Self-improving Synthetic Datasets for Pre-training Classification Models, Proc. CVPR Workshop on Synthetic Data for Computer Vision (SynData4CV), 2024.
• Zhibo Lou, Shinta Otake, Zhengxiao. Li, Rei Kawakami, Nakamasa Inoue, Cubic Knowledge Distillation for Speech Emotion Recognition, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 5705-5709, 2024.
• Sota Miyamoto, Takuma Yagi, Yuto Makimoto, Mahiro Ukai, Yoshitaka Ushiku, Atsushi Hashimoto, Nakamasa Inoue, PolarDB: Formula-Driven Dataset for Pre-Training Trajectory Encoders, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 5465-5469, 2024.
• Ryo Nakamura, Ryu Tadokoro, Eisuke Yamagata, Yusuke Kondo, Kensho Hara, Hirokatsu Kataoka, Nakamasa Inoue, Pseudo-outlier Synthesis Using q-Gaussian Distributions for Out-of-distribution Detection, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3120-3124, 2024.
• Yanhao Bao, Tatsukichi Shibuya, Ikuro Sato, Rei Kawakami and Nakamasa Inoue, Efficient Target Propagation by Deriving Analytical Solution, Proc. AAAI Conference on Artificial Intelligence (AAAI), pp. 11016-11023, 2024.
• Taiki Miyanishi, Fumiya Kitamori, Shuhei Kurita, Jungdae Lee, Motoaki Kawanabe, Nakamasa Inoue CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data, Proc. Neural Information Processing Systems (NeurIPS), pp. 77758-77770, 2023.
• Lei Xu, Rei Kawakami, Nakamasa Inoue, Scale-space Tokenization for Improving the Robustness of Vision Transformers, Proc. ACM Multimedia (ACMMM), pp. 2684-2693, 2023.
• Ryo Nakamura, Hirokatsu Kataoka, Sora Takashima, Edgar Josafat Martinez-Noriega, Rio Yokota, Nakamasa Inoue, Pre-training Vision Transformers with Very Limited Synthesized Images, Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 20360-20369, 2023.
• Risa Shinoda, Ryo Hayamizu, Kodai Nakashima, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka, SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning, Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 20054-20063, 2023.
• Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka, Rio Yokota, Visual Atoms: Pre-training Vision Transformers with Sinusoidal Waves, Proc. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), pp. 18579-18588, 2023.
• Shinta Otake, Rei Kawakami, Nakamasa Inoue, Parameter Efficient Transfer Learning for Various Speech Processing Tasks, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023.
• Keita Goto, Shinta Otake, Rei Kawakami, Nakamasa Inoue, Step Restriction for Improving Adversarial Attacks, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023.
• Toshihiro Ota, Ikuro Sato, Rei Kawakami, Masayuki Tanaka, Nakamasa Inoue, Learning with Partial Forgetting in Modern Hopfield Networks, Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 6661-6673, 2023.
• Tatsukichi Shibuya, Nakamasa Inoue, Rei Kawakami and Ikuro Sato, Fixed-Weight Difference Target Propagation, Proc. AAAI Conference on Artificial Intelligence (AAAI), pp. 9811-9819, 2023.
• Ruoyue Shen, Nakamasa Inoue, and Koichi Shinoda, Text-Guided Object Detector for Multi-modal Video Question Answering, Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1032-1042, 2023.
• Ikuro Sato, Ryota Yamada, Masayuki Tanaka, Nakamasa Inoue, Rei Kawakami, PoF: Post-Training of Feature Extractor for Improving Generalization, Proc. International Conference on Machine Learning (ICML), pp. 19221-19230, 2022.
• Hirokatsu Kataoka, Ryo Hayamizu, Ryosuke Yamada, Kodai Nakashima, Sora Takashima, Xinyu Zhang, Edgar Josafat Martinez-Noriega, Nakamasa Inoue, Rio Yokota, Replacing Labeled Real-image Datasets with Auto-generated Contours, Proc. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), pp. 21232-21241, 2022.
• Tomohiro Hayase, Suguru Yasutomi, Nakamasa Inoue, Downstream Augmentation Generation for Contrastive Learning, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2115-2119, 2022.
• Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh, Pre-training without Natural Images, Springer International Journal of Computer Vision (IJCV), vol. 130, pp. 990-1007, 2022.
• Kodai Nakashima, Hirokatsu Kataoka, Asato Matsumoto, Kenji Iwata, Nakamasa Inoue, Yutaka Satoh, Can Vision Transformers Learn without Natural Images?, Proc. AAAI Conference on Artificial Intelligence (AAAI), pp. 1990-1998, 2022.
• Hirokatsu Kataoka, Kensho Hara, Ryusuke Hayashi, Eisuke Yamagata, Nakamasa Inoue, Spatiotemporal Initialization for 3D CNNs with Generated Motion Patterns, Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1279-1288, 2022.
• Nakamasa Inoue, Tsubasa Maruyama, Keita Goto, Augmentation-Agnostic Regularization for Unsupervised Contrastive Learning with Its Application to Speaker Verification Proc. Asia-Pacific Signal and Information Processing Association Annual Conference (APSIPA), pp. 1993-1998, 2021. (Best Paper Award)
• Hirokatsu Kataoka, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Yutaka Satoh, Formula-Driven Supervised Learning with Recursive Tiling Patterns, Proc. ICCV HTCV Workshop, pp. 4098-4105, 2021.
• Keita Goto, Nakamasa Inoue, Learning VAE with Categorical Labels for Generating Conditional Handwritten Characters, Proc. International Conference on Machine Vision Applications (MVA), 2021.
• Mariana Rodrigues Makiuchi, Tifani Warnita, Nakamasa Inoue, Koichi Shinoda, Michitaka Yoshimura, Momoko Kitazawa, Kei Funaki, Yoko Eguchi, Taishiro Kishimoto, Speech Paralinguistic Approach for Detecting Dementia Using Gated Convolutional Neural Network, IEICE Trans. on Information and Systems, vol. E104–D, no.11, pp. 1930-1940, 2021.
• Ryota Nakazawa, Yuki Minamoto, Nakamasa Inoue, and Mamoru Tanahashi, Species Reaction Rate Modelling based on Physics-Guided Machine Learning, Elsevier Combustion and Flame, vol. 235, pp. 1-11, 2021.
• Nakamasa Inoue, Ryota Yamada, Rei Kawakami, Ikuro Sato, Disentangling Latent Groups of Factors, Proc. IEEE International Conference on Image Processing (ICIP), 2021.
• Nakamasa Inoue, Teacher-Assisted Mini-Batch Sampling for Blind Distillation using Metric Learning, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021.
• Hikaru Nakata, Nakamasa Inoue, Rio Yokota, Self-supervised Continual Pretraining for Class Incremental Image Classification, Proc. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) CLVISION Workshop, 2021.
• Kherlen Jigjid, Chitoshi Tamaoki, Yuki Minamoto, Ryota Nakazawa, Nakamasa Inoue, Mamoru Tanahashi. Data Driven Analysis and Prediction of MILD Combustion Mode, Elsevier Combustion and Flame, vol. 223, pp. 475-485, 2021.
• Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh, Pre-training without Natural Images, Proc. Asian Conference on Computer Vision (ACCV), 2020. (Best Paper Honorable Mention Award)
• Nakamasa Inoue, Graph Grouping Loss for Metric Learning of Face Image Representations, Proc. IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020.
• Nakamasa Inoue, Keita Goto, Semi-Supervised Contrastive Learning with Generalized Contrastive Loss and Its Application to Speaker Recognition, Proc. Asia-Pacific Signal and Information Processing Association Annual Conference (APSIPA), 2020.
• Keita Goto, Nakamasa Inoue, Quasi-Newton Adversarial Attacks on Speaker Verification Systems, Proc. Asia-Pacific Signal and Information Processing Association Annual Conference (APSIPA), 2020.
• Nakamasa Inoue, Keita Goto, Optimizing Speaker Embeddings using Meta-Training Sets, Proc. Asia-Pacific Signal and Information Processing Association Annual Conference (APSIPA), 2020.
• Nakamasa Inoue, Keita Goto, Closed-Form Pre-Training for Small-Sample Environmental Sound Recognition, Proc. Asia-Pacific Signal and Information Processing Association Annual Conference (APSIPA), 2020.
• Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka, Nakamasa Inoue, Augmented Cyclic Consistency Regularization for Unpaired Image-To-Image Translation, Proc. International Conference on Pattern Recognition (ICPR), 2020.
• Nakamasa Inoue, Eisuke Yamagata, Hirokatsu Kataoka. Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data, Proc. International Conference on Pattern Recognition (ICPR), 2020.
• Raden Mu'az Mun'im, Nakamasa Inoue, Koichi Shinoda, Sequence-Level Knowledge Distillation for Model Compression of Attention-Based Sequence-to-Sequence Speech Recognition Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 6151-6155, 2019.
• Nakamasa Inoue, Koichi Shinoda, Few-Shot Adaptation for Multimedia Semantic Indexing, Proc. ACM Multimedia (ACMMM), pp. 1110-1118, 2018.
• Jiacen Zhang, Nakamasa Inoue, Koichi Shinoda, I-vector Transformation Using Conditional Generative Adversarial Networks for Short Utterance Speaker Verification, Proc. Interspeech, pp. 3613-3617, 2018.
• Tifani Warnita, Nakamasa Inoue, Koichi Shinoda, Detecting Alzheimer's Disease Using Gated Convolutional Neural Network from Audio Data, Proc. Interspeech, pp. 1706-1710, 2018.
• Thao Minh Le, Nakamasa Inoue, Koichi Shinoda, A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition, Proc. British Machine Vision Conference (BMVC), 2018.
• Haoyi Zhang, Conggui Liu, Nakamasa Inoue, Koichi Shinoda, Multi-Task Autoencoder for Noise-Robust Speech Recognition, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 5599-5603, 2018.
• Tommi Kerola, Nakamasa Inoue and Koichi Shinoda, Cross-View Human Action Recognition from Depth Maps Using Spectral Graph Sequences, Elsevier Journal of Computer Vision and Image Understanding, vol. 154, pp. 108–126, 2017.
• Yuki Yasui, Nakamasa Inoue, Koji Iwano, and Koichi Shinoda, Multimodal Speech Recognition Using Mouth Images from Depth Camera, Proc. Asia-Pacific Signal and Information Processing Association Annual Conference (APSIPA), pp. 128–132, 2017.
• Conggui Liu, Nakamasa Inoue, and Koichi Shinoda, A Unified Network for Multi-Speaker Speech Recognition with Multi-Channel Recordings, Proc. Asia-Pacific Signal and Information Processing Association Annual Conference (APSIPA), pp. 160–165, 2017.
• Mengxi Lin, Nakamasa Inoue, and Koichi Shinoda, CTC Network with Statistical Language Modeling for Action Sequence Recognition in Videos, Proc. ACM Multimedia Thematic Workshop, 2017.
• Shinya Matsui, Nakamasa Inoue, Yuko Akagi, Goshu Nagino, and Koichi Shinoda, User Adaptation of Convolutional Neural Network for Human Activity Recognition, Proc. European Conference on Signal Processing (EUSIPCO), pp. 753–757, 2017.
• Nakamasa Inoue and Koichi Shinoda, Semantic Indexing for Large-Scale Video Retrieval (Invited Paper), ITE Trans. on Media Technology and Applications, vol. 4, no. 3, pp. 209–217, 2016.
• Tommi Kerola, Nakamasa Inoue and Koichi Shinoda, Graph Regularized Implicit Pose for 3D Human Action Recognition, Proc. Asia-Pacific Signal and Information Processing Association Annual Conference (APSIPA), pp. 155–159, 2016.
• Nakamasa Inoue and Koichi Shinoda, Adaptation of Word Vectors using Tree Structure for Visual Semantics, Proc. ACM Multimedia (ACMMM), 2016.
• Nakamasa Inoue and Koichi Shinoda, Fast Coding of Feature Vectors using Neighbor-To-Neighbor Search, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 38, no. 6, pp. 1170–1184, 2015.
• Nakamasa Inoue and Koichi Shinoda, Vocabulary Expansion Using Word Vectors for Video Semantic Indexing, Proc. ACM Multimedia (ACMMM), 2015.
• Fumito Nishi, Nakamasa Inoue and Koichi Shinoda, Speaker Diarization Using Multi-Modal i-vectors, Proc. Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), 2015.
• Nakamasa Inoue and Koichi Shinoda, n-Gram Models for Video Semantic Indexing, Proc. ACM Multimedia (ACMMM), 2014.
• Tommi Kerola, Nakamasa Inoue, and Koichi Shinoda, Spectral Graph Skeletons for 3D Action Recognition, Proc. Asian Conference on Computer Vision (ACCV), 2014.
• Zhuolin Liang, Nakamasa Inoue and Koichi Shinoda, Event Detection by Velocity Pyramid, Proc. Multimedia Modeling (MMM), 2014.
• Nakamasa Inoue and Koichi Shinoda, q-Gaussian Mixture Models for Image and Video Semantic Indexing, Elsevier Journal of Visual Communication and Image Representation, vol. 24, no. 8, pp. 1450–1457, 2013.
• Yusuke Kamishima, Nakamasa Inoue and Koichi Shinoda, Event Detection in Con- sumer Videos Using GMM Supervectors and SVMs, EURASIP Journal on Image and Video Processing, vol. 51, pp. 1–13, 2013.
• Nakamasa Inoue and Koichi Shinoda, Neighbor-To-Neighbor Search for Fast Coding of Feature Vectors, Proc. IEEE/CVF International Conference on Computer Vision (ICCV), 2013.
• Nakamasa Inoue and Koichi Shinoda, q-Gaussian Mixture Models Based on Non- Extensive Statistics for Image and Video Semantic Indexing, Proc. Asian Conference on Computer Vision (ACCV), 2012.
• Nakamasa Inoue and Koichi Shinoda, A Fast and Accurate Video Semantic-Indexing System Using Fast MAP Adaptation and GMM Supervectors, IEEE Transactions on Multimedia, vol. 14, no. 4, pp. 1196–1205, 2012.
• Yusuke Kamishima, Nakamasa Inoue, Koichi Shinoda, and Shunsuke Sato, Multimedia Event Detection Using GMM Supervectors and SVMs, Proc. IEEE International Conference on Image Processing (ICIP), 2012.
• Nakamasa Inoue and Koichi Shinoda, A Fast MAP Adaptation Technique for GMM- supervector-based Video Semantic Indexing, Proc. ACM Multimedia (ACMMM), 2011.
• Nakamasa Inoue, Tatsuhiko Saito, Koichi Shinoda, and Sadaoki Furui, High-Level Feature Extraction using SIFT GMMs and Audio Models, Proc. International Conference on Pattern Recognition (ICPR), 2010.

• 佐藤 郎真, 井上 中順, 川上 玲, ロボットによる物体再配置の予測における隆起関数を用いた衝突回避, 画像の認識・理解シンポジウム MIRU, 2024.
• 田所 龍, 中村 凌, 山田 亮佑, Yuki M. Asano, Iro Laina, Christian Rupprecht, 井上 中順, 横田 理央, 片岡 裕雄, Scaling Backwards: Minimal Synthetic Pre-training?, 画像の認識・理解シンポジウム MIRU, 2024.
• 大谷 豪, 田所 龍, 山田 亮佑, Yuki M. Asano, Iro Laina, Christian Rupprecht, 井上 中順, 横田 理央, 片岡 裕雄, 青木 義満 画像超解像における学習データ構築の再考, 画像の認識・理解シンポジウム MIRU, 2024.
• 冨田 真央, 佐藤 育郎, 川上 玲, 井上 中順, 池畑 諭, 田中 正行, 深層ネットワークのランダム層選択による転移学習, 画像の認識・理解シンポジウム MIRU, 2024.
• 森合 遼, 井上 中順, 田中 正行, 川上 玲, 池畑 諭, 佐藤 育郎, 分布外データの棄却機能を持つモダンホップフィールドネットワーク, 画像の認識・理解シンポジウム MIRU, 2024.
• Ryo Moriai, Nakamasa Inoue, Msayuki Tanaka, Rei Kawakami, Satoshi Ikehata,Ikuro Satou, Distribution-Aware State Update Rule for Modern Hopfield Networks, Workshop on Symbolic-Neural Learning, 2024.
• Kenji Cari Koga, Nakamasa Inoue, Rei Kawakami, Embodied Agent Guiding Through Context-Fused Images, Workshop on Symbolic-Neural Learning, 2024.
• Ruoyue Shen, Nakamasa Inoue, Koichi Shinoda, Hierarchical Code Generator for Compositional Visual Question Answering, Workshop on Symbolic-Neural Learning, 2024.
• 澁谷 辰吉, 井上 中順, 川上 玲, 佐藤 育郎, 二値重み空間でのBinary Neural Networksの学習, 画像の認識・理解シンポジウム MIRU, 2023. (MIRUインタラクティブ発表賞受賞)
• 篠田 理沙, 速水 亮, 中嶋 航大, 井上 中順, 横田 理央, 片岡 裕雄, 数式ドリブン教師あり学習によるセマンティックセグメンテーション, 画像の認識・理解シンポジウム MIRU, 2023. (MIRU学生奨励賞受賞)
• Ryosuke Yamada, Kensho Hara, Hirokatsu Kataoka, Koshi Makihara, Nakamasa Inoue, Rio Yokota, Yutaka Satoh Formula-Supervised Visual-Geometric Pre-training, 画像の認識・理解シンポジウム MIRU, 2023. (MIRU学生奨励賞受賞)
• Kenji Cari Koga, Nakamasa Inoue, Rei Kawakami, Trajectory Collection with Few-Shot Imitation Learning and Proximal Policy Optimization, 画像の認識・理解シンポジウム MIRU, 2023.
• 佐藤 郎真, 井上 中順, 川上 玲, ロボットによる物体再配置における多変量正規分布を用いた衝突回避 画像の認識・理解シンポジウム MIRU, 2023.
• 髙橋 秀弥, 井上 中順, 横田 理央, 片岡 裕雄, 前田 英作, 学習過程における形状・テクスチャ偏重度の推移と事前学習データセットとの関係について, 画像の認識・理解シンポジウム MIRU, 2023.
• 速水 亮, 高島 空良, 井上 中順, 片岡 裕雄, 横田 理央, Visual Atoms: 正弦波の輪郭表現によるVision Transformerの事前学習, 画像センシングシンポジウム SSII, 2023. (SSIIオーディエンス賞受賞)
• 中村 凌, 片岡 裕雄, 高島 空良, Martinez-Noriega Edgar Josafat, 横田 理央, 井上 中順, 限られた合成画像を用いたVision Transformerの事前学習, 画像センシングシンポジウム SSII, 2023. (SSIIオーディエンス賞受賞)
• 浅倉 拓也, 井上 中順, 横田 理央, 篠田 浩一, 受容野の自動最適化によるモードに適応的なTransformerの開発, 人工知能学会 JSAI, 2023.
• 髙橋 秀弥, 井上 中順, 横田 理央, 片岡 裕雄, 前田 英作, 画像識別における形状・テクスチャ偏重度と二重降下現象の関係について, パターン認識・メディア理解研究会 PRMU, 2023.
• Jungdae Lee, Rei Kawakami, Nakamasa Inoue, Similarity Based Attention on a Hypersphere for Vision Transformers, パターン認識・メディア理解研究会 PRMU, 2022.
• 杉山 佳史, 片岡 裕雄, 横田 理央, 井上 中順, 敵対的距離学習モジュールを用いた特徴変動に頑健な画像認識のための対照学習, パターン認識・メディア理解研究会 PRMU, 2022.
• 篠田 理沙, 速水 亮, 中嶋 航大, 井上 中順, 片岡 裕雄, セグメンテーションタスクにおける実画像を用いない事前学習, ビジョン技術の実利用ワークショップ ViEW, 2022. (若手奨励賞受賞)
• 田所 龍, 片岡 裕雄, 川上 玲, 横田 理央, 井上 中順, 蒸留画像による事前学習効果についての検討, ビジョン技術の実利用ワークショップ ViEW, 2022.
• 宮本 蒼太, 八木 拓真, 牛久 祥孝, 橋本 敦史, 井上 中順, 手の軌道特徴を用いた一人称視点料理動画における詳細動作認識, パターン認識・メディア理解研究会 PRMU, 2022. (PRMU月間ベストプレゼンテーション賞受賞)
• 澁谷 辰吉, 佐藤 育郎, 川上 玲, 井上 中順, Random Matrixによる固定逆伝播ネットワークを用いたTarget Propagation派生手法の提案, 画像の認識・理解シンポジウム MIRU, 2022. (MIRUインタラクティブ発表賞受賞)
• 山田 陵太, 佐藤 育郎, 田中 正行, 井上 中順, 川上 玲, 深層モデルの汎化性能改善を目的とした特徴抽出器の事後学習, 画像の認識・理解シンポジウム MIRU, 2022. (MIRU長尾賞受賞, テレコムシステム技術学生賞受賞)
• Toshihiro Ota, Ikuro Sato, Rei Kawakami, Masahiro Tanaka, Nakamasa Inoue, Learning with Partial Forgetting in Modern Hopfield Networks, 画像の認識・理解シンポジウム MIRU, 2022.
• 江良 真結子, 井上 中順, 篠田 浩一, 細川 稜平, 村田 勝寛, 庭野 聖史, 谷津 陽一, 河合 誠之, ロバスト主成分分析に基づく劣化CCDカメラ画像のノイズ除去, 日本天文学会春季年会, 2022.
• 伊藤 尚泰, 村田 勝寛, 細川 稜平, 笹田 真人, 庭野 聖史, 谷津 陽一, 河合 誠之, 篠田 浩 一, 井上 中順, 伊藤 亮介, 下川辺 隆史, MITSuME望遠鏡画像に対する深層学習を用いた突発天体検知システムの構築, 日本天文学会春季年会, 2022.
• 松本 晨人, 山縣 英介, 井上 中順, 片岡 裕雄, 佐藤 雄隆, 数式ベースの事前学習用データセットの特性評価, 画像センシングシンポジウム SSII, 2020.
• Kodai Nakashima, Hirokatsu Kataoka, Asato Matsumoto, Kenji Iwata, Nakamasa Inoue, Can Vision Transformers Learn without Natural Images?, 画像の認識・理解シンポジウム MIRU, 2021.
• Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka, Nakamasa Inoue, Consistency Regularization using Data Augmentation for Cycle-Consistent GANs, 画像の認識・理解シンポジウム MIRU, 2021.
• 井上 中順, 限られたデータからの深層学習 (チュートリアル講演), 画像の認識・理解シンポジウム MIRU, 2021.
• 井上 中順, 限られたデータからの学習法 (招待講演), 精密工学会 定例研究会, 2020.
• Jigjid Kherlen, Yuki Minamoto, Nakazawa Ryota, Inoue Nakamasa, Tanahashi Mamoru, A DNN based identifier for MILD combustion mode in an LES context, 日本伝熱学会 日本伝熱シンポジウム, 2020.
• 井上 中順, 限られたデータからの深層学習 (オーガナイズドセッション), 画像センシングシンポジウム SSII, 2020.
• Kengo Machida, Nakamasa Inoue, Koichi Shinoda. KL統計量に基づくニューラルネットワークのプルーニング, 画像の認識・理解シンポジウム MIRU, 2019.

Access

G3-1013 (Mail-Box:G3-55), Institute of Science Tokyo, 4259 Nagatsuta, Midori-ku, Yokohama, 226-8502, Japan.

〒226-8502 横浜市緑区長津田町4259 東京科学大学 G3棟1013号室 (ポスト:G3-55)