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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, IPSJ-CVIM, IPSJ-CGVI, IPSJ-DCC | 2024-11-29 11:25 |
Fukui | (Fukui, Online) (Primary: On-site, Secondary: Online) |
Self-supervised Contrastive Learning for Remote Sensing Imagery with Perlin noise injection Riku Okazawa, Eisaku Maeda (Tokyo Denki Univ.)  PRMU2024-11 |
With the increasing use of satellites, the application of remote sensing (RS) imagery is rapidly advancing. Deep learnin... [more] | PRMU2024-11 pp.19-24 |
| CS | 2024-11-06 14:30 |
Osaka | Osaka Public University I-site Namba C1 Conference Room (Osaka) | [Invited Lecture]
Digital-twin framework for micro-mobility vehicles toward driving risk prediction against cyber-physical security attacks Shunsuke Sato, Ryoichi Shinkuma (SIT) CS2024-47 |
The widespread adoption of micro-mobility services necessitates the assurance of operational safety. Current initiatives... [more] | CS2024-47 pp.5-8 |
| NLC | 2024-09-02 13:10 |
Hokkaido | Hokkaido University. The Clark Memorial Student Center. (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Extracting Conceptual Relation Graphs for Solving Story Tasks Kei Okada, Rafal Rzepka, Toshihiko Itoh (Hokkaido Univ.) NLC2024-1 |
Methods using Retrieval Augmented Generation (RAG) have attracted attention for many tasks, including QA. The ultimate g... [more] | NLC2024-1 pp.1-5 |
| RCC, ISEC, IT, WBS | 2024-03-13 - 2024-03-14 |
Osaka | Osaka Univ. (Suita Campus) (Osaka) | Improving The Classification Accuracy of Imaged Malware through Data Expansion Kaoru Yokobori, Hiroki Tanioka, Masahiko Sano, Kenji Matsuura, Tetsushi Ueta (Tokushima Univ.) IT2023-115 ISEC2023-114 WBS2023-103 RCC2023-97 |
Although malware-based attacks have existed for years, malware infections increased in 2019 and 2020. One of the reaso... [more] |
IT2023-115 ISEC2023-114 WBS2023-103 RCC2023-97 pp.259-264 |
| NLP, MSS | 2024-03-13 17:20 |
Misc. | Kikai-Shinko-Kaikan Bldg. (Misc.) | Application of Data Augmentation in Japanese Foundation Models Kazuki Era, Hidehiro Nakano (Tokyo City Univ.) MSS2023-84 NLP2023-136 |
One of the recent topics is data augmentation. Data augmentation is a method of augmenting training data to improve the ... [more] | MSS2023-84 NLP2023-136 pp.66-69 |
| NLC, IPSJ-NL | 2024-03-10 10:50 |
Hyogo | (Hyogo, Online) (Primary: On-site, Secondary: Online) |
Quality Assessment for debate using combined models and data augmentation Shunsuke Hashiguchi, Kazutaka Shimada (KIT) NLC2023-23 |
Recently, the incorporation of group debates has emerged as a strategic approach for measuring communication ability wit... [more] | NLC2023-23 pp.1-6 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 09:30 |
Okinawa | (Okinawa, Online) (Primary: On-site, Secondary: Online) |
Vocal tract length perturbation-based pseudo-speaker augmentation for automatic speaker verification Tomoka Wakamatsu, Sayaka Shiota, Hitoshi Kiya (Tokyo Metropolitan Univ.) EA2023-61 SIP2023-108 SP2023-43 |
In recent years, deep neural network (DNN)-based automatic speaker verification (ASV) systems have become mainstream. Da... [more] | EA2023-61 SIP2023-108 SP2023-43 pp.1-6 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-03-01 09:30 |
Okinawa | (Okinawa, Online) (Primary: On-site, Secondary: Online) |
Evaluation of Automatic Speech Recognition for Deaf and Hard-of-Hearing People by Speaker Adaptation. Kaito Takahashi, Takahiro Kinouchi, Yukoh Wakabayashi (TUT), Kengo Ohta (NITAC), Akio Kobayashi (Yamato Univ.), Norihide Kitaoka (TUT) EA2023-102 SIP2023-149 SP2023-84 |
Communication between normal-hearing people and the deaf is generally used sign language, written communication, and spe... [more] | EA2023-102 SIP2023-149 SP2023-84 pp.244-249 |
| PRMU, MVE, VRSJ-SIG-MR, IPSJ-CVIM | 2024-01-26 15:34 |
Kanagawa | Keio Univ. (Hiyoshi Campus) (Kanagawa) | Comparison of Imbalanced Data Handling Techniques in Emotion Estimation of Expressway Service Area Workers using Stacking Ensemble Learners for Complex Decision Boundaries Akihiro Sato, Satoki Ogiso, Ryosuke Ichikari, Takeshi Kurata (AIST) PRMU2023-47 |
Estimating emotions of workers is promising to promote health and productivity management, while it has difficulty in c... [more] | PRMU2023-47 pp.40-45 |
| HCGSYMPO (2nd) |
2023-12-11 - 2023-12-13 |
Fukuoka | Asia pacific Import Mart (Kitakyushu) (Fukuoka, Online) (Primary: On-site, Secondary: Online) |
Facial Image Data Augmentation using Variational Auto-Encoder with Geometric Features Takanori Sano, Hideaki Kawabata (Keio Univ.) |
Numerous studies have been conducted in the field of psychology on the factors that influence facial impressions. In rec... [more] | |
| SeMI, RCS, RCC, NS, SR (Joint) |
2023-07-13 13:50 |
Osaka | Osaka University Nakanoshima Center + Online (Osaka, Online) (Primary: On-site, Secondary: Online) |
A GAN-Based Data Augmentation Approach to Improve Heart Rate Range Classification via Doppler Radar Danyuan Yu, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.) SeMI2023-31 |
In this work, a novel data augmentation framework is proposed for improving heart rate (HR) range classification using D... [more] | SeMI2023-31 pp.46-51 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2023-06-23 13:50 |
Tokyo | (Tokyo, Online) (Primary: On-site, Secondary: Online) |
Data Augmentation by Synthesised Voice for Deep Learning-based A Cappella Separation Kyoka Kazama (TMU), Yuma Kinoshita (Tokai Univ.), Natsuki Ueno, Nobutaka Ono (TMU) SP2023-4 |
In this study, we examine efficacy of training data augmentation for a cappella singing voice separation using deep lear... [more] | SP2023-4 pp.14-19 |
| CCS | 2023-03-26 13:35 |
Hokkaido | RUSUTSU RESORT (Hokkaido) | Analysis of learning performance in CycleGAN by applying data augmentation to few data Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-72 |
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] | CCS2022-72 pp.54-58 |
| EMCJ, MICT (Joint) |
2023-03-17 14:15 |
Tokyo | Kikai-Shinko-Kaikan Bldg (Tokyo, Online) (Primary: On-site, Secondary: Online) |
An Effect of Data Augmentation using 3D Models in Machine Lipreading on Recognition Accuracy Kazuma Kimura, Kenko Ota (NIT) MICT2022-59 |
In this study, we investigate the use of a three-dimensional model of a speaker's face as a data augmentation method for... [more] | MICT2022-59 pp.17-21 |
| IMQ, IE, MVE, CQ (Joint) [detail] |
2023-03-17 13:40 |
Okinawa | Okinawaken Seinenkaikan (Naha-shi) (Okinawa, Online) (Primary: On-site, Secondary: Online) |
Applicability of Motion Transfer to Generation of Knee Joint Lateral Thrust Video Yusuke Okamura, Dan Mikami (KUTE-TOKYO), Takuya Ibara, Koji Fujita (TMDU) IMQ2022-76 IE2022-153 MVE2022-106 |
This study uses Motion Transfer technology, which has been gaining attention in recent years, to simulate the gait of a ... [more] | IMQ2022-76 IE2022-153 MVE2022-106 pp.290-295 |
| NC, MBE (Joint) |
2023-03-14 10:20 |
Tokyo | The Univ. of Electro-Communications (Tokyo, Online) (Primary: On-site, Secondary: Online) |
Study on a robust and accurate deep learning method Shoma Noguchi, Shogo Taneda, Yukari Yamauchi (Nihon Univ.) NC2022-101 |
In deep learning, there are many hyperparameters that must be determined in advance, and it is known that the accuracy v... [more] | NC2022-101 pp.54-59 |
| NC, MBE (Joint) |
2023-03-14 16:15 |
Tokyo | The Univ. of Electro-Communications (Tokyo, Online) (Primary: On-site, Secondary: Online) |
Generation and inpainting of Kuzushiji image data using Boltzmann Machines Hiroki Ikoma (NAIST), Mauricio Bermudez, Minho Lee (KNU), Kazushi Ikeda (NAIST) NC2022-108 |
It is almost impossible for the average person to read Kuzushiji today. For this reason, there is a need to develop tool... [more] | NC2022-108 pp.94-98 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 09:00 |
Hokkaido | Future University Hakodate (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Toward Regularizing Neural Networks with Meta-Learning Generative Models Shin'ya Yamaguchi (NTT/Kyoto Univ.), Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai (NTT), Hisashi Kashima (Kyoto Univ.) PRMU2022-58 IBISML2022-65 |
This paper investigates methods for improving generative data augmentation for deep learning. Generative data augmentati... [more] | PRMU2022-58 IBISML2022-65 pp.1-6 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 10:25 |
Hokkaido | Future University Hakodate (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Cell mitosis detection with partial annotation Kazuya Nishimura (Kyushu Univ.), Ami Katanaya, Shinichiro Chuma (Kyoto Univ.), Ryoma Bise (Kyushu Univ.) PRMU2022-67 IBISML2022-74 |
(To be available after the conference date) [more] | PRMU2022-67 IBISML2022-74 pp.48-53 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 17:30 |
Hokkaido | Future University Hakodate (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
A Study on Data Augmentation by Pixel Value Transformation for Product Image Retrieval DNN Yu Okamoto, Masaki Kishibe, Toshikazu Wada (Wakayama Univ.) PRMU2022-94 IBISML2022-101 |
We are developing a product image retrieval DNN for supporting shelving allocation tasks. To train the DNN, product imag... [more] | PRMU2022-94 IBISML2022-101 pp.181-186 |
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