Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
NC, MBE (Joint) |
2024-03-12 13:55 |
Tokyo |
The Univ. of Tokyo (Primary: On-site, Secondary: Online) |
Identification of filamentous fungi by segmentation models using consistency regularization and classmix Taiga Shimizu (Yamanashi Univ.), Waleed Asghar (Oklahoma State Univ.), Ryota Kataoka, Motonobu Hattori (Yamanashi Univ.) NC2023-57 |
In agriculture, soil diagnosis is necessary to protect the environment. However, since current diagnostic methods are no... [more] |
NC2023-57 pp.81-86 |
MI |
2024-03-03 17:18 |
Okinawa |
OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
3D shape reconstruction of colon with model-based unsupervised depth estimation Natsu Onozaka (Nagoya Univ.), Hayato Itoh (Fukuoka Univ.), Masahiro Oda (Nagoya Univ.), Masashi Misawa (Showa Univ.), Yuichi Mori (UiO), Shin-ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.) MI2023-60 |
We propose unsupervised trainig for the pose estimation in 3D reconstrcution of the colon from colonoscopic images by cl... [more] |
MI2023-60 pp.87-90 |
MI |
2024-03-04 09:00 |
Okinawa |
OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Distance-informed adversarial learning for metal artifact reduction Daisuke Shigemori, Megumi Nakao (Kyoto Univ.) MI2023-62 |
In this study, we propose an adversarial learning framework that utilises distance information from metal to reduce CT m... [more] |
MI2023-62 pp.95-98 |
MI |
2024-03-04 15:58 |
Okinawa |
OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Application of representations obtained by self-supervised learning of hierarchical ViT to discriminate between good and bad breast tumors. Akiomi Ishikawa (NIT), Kouji Arihiro (HIrosima Univ), Hidekata Hontani (NIT) MI2023-88 |
In this paper, I report a method to apply the representation of pathological microscopic images obtained by self-supervi... [more] |
MI2023-88 pp.184-185 |
NS, IN (Joint) |
2024-02-29 11:10 |
Okinawa |
Okinawa Convention Center |
An unsupervised online learning-based traffic classification and anomaly detection method for 5G-IIoT systems Yuxuan Shi, Qianqian Pan, Akihiro Nakao (U Tokyo) NS2023-188 |
In the context of Society 5.0, the evolution of the Internet of Things (IoT) and its ever growing demands of massive Mac... [more] |
NS2023-188 pp.96-102 |
DE, IPSJ-DBS |
2023-12-26 14:00 |
Tokyo |
Institute of Industrial Science, The University of Tokyo |
Interpretation of unsupervised clustering based on XAI Yu Sasaki, Fumiaki Saitoh (CIT) DE2023-28 |
Explainable Artificial Intelligence (XAI) aims to introduce transparency and interpretability into the decision-making o... [more] |
DE2023-28 pp.1-6 |
QIT (2nd) |
2023-12-18 14:30 |
Okinawa |
OIST (Primary: On-site, Secondary: Online) |
Advantage of Quantum Machine Learning from General Computational Advantages Hayata Yamasaki, Natsuto Isogai, Mio Murao (UTokyo) |
Demonstrating the existence of general learning problems where machine learning using quantum computers exhibits rigorou... [more] |
|
WIT, HI-SIGACI |
2023-12-07 11:15 |
Tokyo |
AIST Tokyo Waterfront (TBD) |
On Modeling Daily Activities of the Elderly Living Alone Using Markov Series of Dirichlet Multinomial Mixture Models Ken Sadohara (AIST) WIT2023-30 |
To develop smart home technology designed to analyze the activity of residents based on the logs of installed sensors, a... [more] |
WIT2023-30 pp.31-36 |
SP, NLC, IPSJ-SLP, IPSJ-NL [detail] |
2023-12-03 11:05 |
Tokyo |
Kikai-Shinko-Kaikan Bldg. (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Self-supervised learning model based emotion transfer and intensity control technology for expressive speech synthesis Wei Li, Nobuaki Minematsu, Daisuke Saito (Univ. of Tokyo) NLC2023-21 SP2023-41 |
Emotion transfer techniques, which transfersba the speaking style from the reference speech to the target speech, are wi... [more] |
NLC2023-21 SP2023-41 pp.43-48 |
MI, MICT |
2023-11-14 15:00 |
Fukuoka |
|
Pre-training without natural images for Cystoscopic AI Diagnosis of Bladder Cancer Ryuunosuke Kounosu (AIST/Toho Univ.), Wonjik Kim (AIST), Atsushi Ikeda (Univ. of Tsukuba), Hirokazu Nosato (AIST), Yuu Nakajima (Toho Univ.) MICT2023-34 MI2023-27 |
When developing AI models, it is sometimes difficult to collect sufficient training data. In these cases, pre-trained AI... [more] |
MICT2023-34 MI2023-27 pp.37-40 |
BioX |
2023-10-13 10:20 |
Okinawa |
Nobumoto Ohama Memorial Hall |
Discrimination between Real and Generated Gestures of Speakers
-- An Attempt to Improve Generalization Performance in Unseen Generation Methods through Self-Supervised Learning -- Geng Mu (AGU), Naoshi Kaneko (TDU), Kazuhiko Sumi (AGU) BioX2023-67 |
Currently, discerning artificially generated misinformation is a critical societal challenge, with research progressing ... [more] |
BioX2023-67 pp.44-49 |
IA |
2023-09-22 10:40 |
Hokkaido |
Hokkaido Univeristy (Primary: On-site, Secondary: Online) |
OLIViS: An OSINT-Based Lightweight Method for Identifying Video Content Services for Capacity Planning in Backbone ISPs Yuki Tamura, Fumio Teraoka, Takao Kondo (Keio Univ.) IA2023-23 |
As of 2022, 66% of Internet traffic is generated by video content services, among which Netflix and YouTube are the domi... [more] |
IA2023-23 pp.75-82 |
IBISML |
2023-09-08 |
Osaka |
Osaka Metropolitan University (Nakamozu Campus) (Primary: On-site, Secondary: Online) |
Proposal of a Learning Time Reduction Algorithm in Machine Learning through Input Data Abstraction Tsubasa Sakoda IBISML2023-27 |
In this research, I attempt to reduce the learning time of machine learning by using simple calculation such as averagin... [more] |
IBISML2023-27 pp.12-15 |
IBISML |
2023-09-08 13:25 |
Osaka |
Osaka Metropolitan University (Nakamozu Campus) (Primary: On-site, Secondary: Online) |
Consideration of Negative Samples in Contrastive Learning Daiki Ishiguro, Tomoko Ozeki (Tokai Univ.) IBISML2023-28 |
Contrastive learning has achieved accuracy comparable to supervised learning. In this method, the transformed image pair... [more] |
IBISML2023-28 pp.16-21 |
CQ, MIKA (Joint) (2nd) |
2023-08-31 10:50 |
Fukushima |
Tenjin-Misaki Sports Park |
[Poster Presentation]
Study on the Effectiveness of Building TCP Throughput Prediction Model using Federated Learning Han Nay Aung, Hiroyuki Ohsaki (Kwansei Gakuin Univ) |
In the realm of communication networks, ensuring accurate forecasts for the performance of TCP flows is essential to ach... [more] |
|
PRMU, IPSJ-CVIM |
2023-05-19 15:40 |
Aichi |
(Primary: On-site, Secondary: Online) |
Object-Centric Representation Learning with Attention Mechanism Hidemoto Nakada, Hideki Asoh (AIST) PRMU2023-13 |
For object-centric representation learning, several slot-based methods, that separate objects using masks and learn the ... [more] |
PRMU2023-13 pp.68-73 |
NLP, MSS |
2023-03-17 16:05 |
Nagasaki |
(Primary: On-site, Secondary: Online) |
Lightweighting Noisy Student Semi-Supervised Learning by Applying MobileNet Yuga Morishima, Hidehiro Nakano (Tokyo City Univ.) MSS2022-108 NLP2022-153 |
Recently, Convolutional Neural Networks (CNNs) have attracted much attention in various fields such as image classificat... [more] |
MSS2022-108 NLP2022-153 pp.220-224 |
MI |
2023-03-06 17:04 |
Okinawa |
OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Rotation-Equivariant CNN for Medical Image Processing Applications Ryota Ogino, Kugler Mauricio, Tatsuya Yokota, Hidekata Hontani (NITech) MI2022-96 |
In this study, we report an attempt to use a Rotation-Equivariant CNN to organize image data whose rotation direction an... [more] |
MI2022-96 pp.113-114 |
MI |
2023-03-07 15:38 |
Okinawa |
OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
A Denoising Method for Low Dose CT by Iterative Processing Using Self-Supervised Learning Yuki Sato, Hiroyuki Kudo (Univ of Tsukuba) MI2022-121 |
In recent years, patient exposure has become an issue, and low-dose CT, which reduces the amount of radiation irradiated... [more] |
MI2022-121 pp.192-193 |
PRMU, IBISML, IPSJ-CVIM [detail] |
2023-03-02 11:05 |
Hokkaido |
Future University Hakodate (Primary: On-site, Secondary: Online) |
On the Effectiveness of Formula-Driven Supervised Learning for Medical Image Tasks Ryuto Endo, Shuya Takahashi, Eisaku Maeda (TDU) PRMU2022-71 IBISML2022-78 |
Deep learning for image information processing often uses manually maintained natural image data. However, these data ha... [more] |
PRMU2022-71 IBISML2022-78 pp.71-75 |