Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
ITE-ME, ITE-IST, BioX, SIP, MI, IE [detail] |
2024-06-07 09:30 |
Niigata |
Nigata University (Ekinan-Campus "TOKIMATE") |
A pre-trained representation learning model can be used to decode speech from intracranial recordings Shoya Murakami, Shuji Komeiji, Kai Shigemi (TUAT), Takumi Mitsuhashi, Yasushi Iimura, Hiroharu Suzuki, Hidenori Sugano (Juntendo Univ.), Koichi Shinoda (Tokyo Tech), Toshihisa Tanaka (TUAT) SIP2024-5 BioX2024-5 IE2024-5 MI2024-5 |
Deep learning has been shown to be effective in decoding the content of a speaker's speech from recordings of brain acti... [more] |
SIP2024-5 BioX2024-5 IE2024-5 MI2024-5 pp.23-28 |
NLP, CCS |
2024-06-06 12:10 |
Fukuoka |
West Japan General Exhibition Center AIM |
Detection of Atrial Fibrillation from ECGs Using Unsupervised Learning Kentaro Sakai, Hiroyuki Kitajima (Kagawa Univ) NLP2024-20 CCS2024-7 |
In this study, the unsupervised learning method (One Class Support Vector Machine) was used to detect atrial fibrillatio... [more] |
NLP2024-20 CCS2024-7 pp.29-30 |
SeMI, IPSJ-ITS, IPSJ-MBL, IPSJ-DPS |
2024-05-17 09:00 |
Okinawa |
|
A basic study on human re-identification using 3D point cloud data focusing on body shape characteristics Shintaro Otsudo, Hiroaki Morino (SIT) SeMI2024-5 |
At indoor events, it is useful to be able to analyze the paths taken by people in order to design appropriate booth layo... [more] |
SeMI2024-5 pp.20-24 |
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]
Representations obtained by self-supervised learning of hierarchical ViT to discriminate between benign and malignant 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 |