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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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) |
(To be available after the conference date) [more] | ||
| 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 |
| 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 |
| 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 |
| 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 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-02-28 17:15 |
Okinawa | (Primary: On-site, Secondary: Online) |
DNN-based Noise Reduction Using Noise Signal for Target Signal Ryota Hiromasa, Hien Ohnaka, Ryoichi Miyazaki (NITTC) EA2022-93 SIP2022-137 SP2022-57 |
This study proposes a DNN-based noise reduction method that uses noise signals instead of clean speech signals as the ta... [more] | EA2022-93 SIP2022-137 SP2022-57 pp.101-106 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 11:00 |
Okinawa | (Primary: On-site, Secondary: Online) |
Analysis of Noisy-target Training for DNN-based speech enhancement and investigation towards its practical use Takuya Fujimura, Tomoki Toda (Nagoya Univ.) EA2022-112 SIP2022-156 SP2022-76 |
Deep neural network (DNN)-based speech enhancement usually uses a clean speech as a training target. However, it is hard... [more] | EA2022-112 SIP2022-156 SP2022-76 pp.221-226 |
| ISEC, SITE, LOIS | 2022-11-18 16:20 |
Online | Online | Efficient workers' behavior segmentation with GP-HSMM Toshiyuki Hatta, Tsubasa Tomoda, Tetsuya Tamaki, Shotaro Miwa (Mitsubishi Electric Corp.) ISEC2022-43 SITE2022-47 LOIS2022-27 |
(To be available after the conference date) [more] | ISEC2022-43 SITE2022-47 LOIS2022-27 pp.79-84 |
| SAT, RCS (Joint) |
2022-08-26 11:40 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Inter-cell Interference Control by Joint Transmit Power and Transmit Beamforming Control based on Machine Learning Naoto Tamada, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech) RCS2022-118 |
In mobile communications, densely deployed small cell systems using the same frequency band are expected to increase the... [more] | RCS2022-118 pp.120-125 |
| AI | 2022-07-04 16:00 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Unsupervised Learning of a Dynamic Task Ordering Model for Crowdsourcing Ryo Yanagisawa (Waseda Univ.), Susumu Saito, Teppei Nakano (ifLab Inc.), Tetsunori Kobayashi, Tetsuji Ogawa (Waseda Univ.) AI2022-14 |
An unsupervised learning method for a dynamic task ordering model that optimizes the number of orders according to the d... [more] | AI2022-14 pp.72-76 |
| CCS, NLP | 2022-06-09 14:15 |
Osaka | (Primary: On-site, Secondary: Online) |
Improvement of Recognition Accuracy by Sequential Execution of Unsupervised Learning and Semi-supervised Learning Hiroki Murakami, Hidehiro Nakano (Tokyo City Univ.) NLP2022-4 CCS2022-4 |
In this study, we propose a sequential learning method that improves recognition accuracy by alternately utilizing the k... [more] | NLP2022-4 CCS2022-4 pp.17-22 |
| SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] | 2022-05-19 13:00 |
Kumamoto | Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
[Special Talk]
Integrative technologies of mathematical modeling and deep Learning
-- Three strategies to distill inductive biases -- Tomoya Sakai (Nagasaki Univ.) SIP2022-9 BioX2022-9 IE2022-9 MI2022-9 |
(To be available after the conference date) [more] | SIP2022-9 BioX2022-9 IE2022-9 MI2022-9 pp.49-54 |
| MSS, NLP | 2022-03-29 09:40 |
Online | Online | Effects of sparse connections in spiking neural networks for unsupervised pattern recognition Hiroki Shinagawa, Kantaro Fujiwara, Gouhei Tanaka (Univ. of Tokyo) MSS2021-69 NLP2021-140 |
Recently, the spiking neural network (SNN) models, which compute using spatio-temporal information representation by neu... [more] | MSS2021-69 NLP2021-140 pp.71-76 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 11:30 |
Online | Online | ITS2021-28 IE2021-37 |
The dynamic range of electronic imaging is orders of magnitudes smaller than that of human vision. To obtain images of h... [more] | ITS2021-28 IE2021-37 pp.19-24 |
| MI | 2022-01-26 13:00 |
Online | Online | Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.) MI2021-59 |
Improving of personal computer performance, it is possible for healthcare workers and related researchers to support for... [more] | MI2021-59 pp.59-64 |
| PRMU | 2021-12-16 11:00 |
Online | Online | Anomaly Detection using PatchCore with Self-attention module Yuki Takena (Shizuoka Univ.), Yoshiki Nota, Rinpei Mochizuki (Meidensya Corp.), Itaru Matsumura (Railway Technical Research Inst.), Gosuke Ohashi (Shizuoka Univ.) PRMU2021-29 |
In recent years, in visual inspection of industrial products using deep learning, There are many models that achieve exc... [more] | PRMU2021-29 pp.31-36 |
| PRMU | 2021-12-16 16:45 |
Online | Online | Verification of Cyclical Annealing for Object-Oriented Representation Learning Atsushi Kobayashi (Waseda Univ.), Hideki Tsunashima (Waseda Univ./AIST), Takehiko Ohkawa (The Univ. of Tokyo), Hiroaki Aizawa (Hiroshima Univ.), Qiu Yue, Hirokatsu Kataoka (AIST), Shigeo Morishima (Waseda Univ.) PRMU2021-39 |
Object-oriented Representation Learning is a method for obtaining images for each object and background part from an ima... [more] | PRMU2021-39 pp.83-87 |
| SIP | 2021-08-23 13:25 |
Online | Online | A study on transfer learning in unsupervised anomalous sound detection based on deep metric learning considering variance of normal data Hiroki Narita, Akira Tamamori (AIT) SIP2021-28 |
In recent years, anomaly detection research in the field of computer vision has focused on methods based on transfer lea... [more] | SIP2021-28 pp.5-10 |
| CS | 2021-07-16 09:40 |
Online | Online | Joint Transmit Power and Beamforming Control based on Unsupervised Machine Learning for MIMO Wireless Communication Networks Naoto Tamada, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech) CS2021-29 |
In mobile communications, densely deployed cell systems are expected to improve the system capacity drastically. However... [more] | CS2021-29 pp.63-68 |
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