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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SIP | 2022-08-25 14:33 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Structured Deep Image Prior with Interscale Thresholding Jikai Li, Shogo Muramatsu (Niigata Univ.)  SIP2022-55 |
This work proposes a novel image denoising technique inspired by the deep image prior (DIP) method. Our contribution is ... [more] | SIP2022-55 pp.31-36 |
| SIP | 2022-08-26 15:33 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Locally-Structured Unitary Network to Capture Tangent Spaces of Manifold Godage Yasas, Shogo Muramatsu (Niigata Univ.) SIP2022-75 |
This work proposes a unique linear transform, locally-structured unitary network (LSUN), that captures tangent spaces of... [more] | SIP2022-75 pp.129-133 |
| 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 |
| MI | 2022-07-08 17:00 |
Hokkaido | (Primary: On-site, Secondary: Online) |
[Short Paper]
Weakly-Supervised Focal Liver Lesion Detection in CT Images He Li, Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin, Ruofeng Tong, Hongjie Hu (Zhejiang Univ.), Akira Furukawa (Tokyo Metropolitan Univ.), Shuzo Kanasaki (Koseikai Takeda Hospital), Yen-Wei Chen (Ritsumeikan Univ.) MI2022-40 |
Convolutional neural networks have been widely used for anomaly detection and one of their most common methods is autoen... [more] | MI2022-40 pp.30-33 |
| 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 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-17 15:00 |
Online | Online | SP2022-13 |
We investigate the method for unsupervised learning of artifacts correction networks used for post-processing of Multi B... [more] | SP2022-13 pp.49-54 |
| SP, IPSJ-MUS, IPSJ-SLP [detail] | 2022-06-18 15:00 |
Online | Online | Unsupervised Training of Sequential Neural Beamformer Using Blindly-separated and Non-separated Signals Kohei Saijo, Tetsuji Ogawa (Waseda Univ.) SP2022-25 |
We present an unsupervised training method of the sequential neural beamformer (Seq-NBF) using the separated signals fro... [more] | SP2022-25 pp.110-115 |
| 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 |
| SIS, IPSJ-AVM | 2022-06-09 15:00 |
Fukuoka | KIT(Wakamatsu Campus) (Primary: On-site, Secondary: Online) |
[Invited Talk]
Advanced applications of machine learning techniques towards high-performance and cost-effective visual inspection AI Terumasa Tokunaga (Kyutech) SIS2022-6 |
Visual inspection is an essential step for quality control in manufacturing. Recently, many researchers have shown great... [more] | SIS2022-6 p.30 |
| SeMI, IPSJ-DPS, IPSJ-MBL, IPSJ-ITS | 2022-05-26 13:45 |
Okinawa | (Primary: On-site, Secondary: Online) |
Unsupervised Learning-based Non-invasive Fetal ECG Signal Quality Assessment Xintong Shi, Kohei Yamamoto, Tomoaki Ohtsuki (Keio Univ.), Yutaka Matsui, Kazunari Owada (Atom Medical Co., Ltd.) SeMI2022-4 |
For fetal heart rate (FHR) monitoring, the non-invasive fetal electrocardiogram (FECG) obtained from abdomen surface ele... [more] | SeMI2022-4 pp.15-19 |
| 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 |
| PRMU, IPSJ-CVIM | 2022-03-10 09:15 |
Online | Online | Unsupervised adaptation of appearance-based gaze estimation models for domains with different label distributions. Takuru Shimoyama, Yusuke Sugano (The Univ. of Tokyo) PRMU2021-61 |
The annotation of gaze estimation is time-consuming, and it is not easy to collect training data under the exact same li... [more] | PRMU2021-61 pp.7-12 |
| PRMU, IPSJ-CVIM | 2022-03-11 17:10 |
Online | Online | PRMU2021-90 |
No English abstract [more] | PRMU2021-90 pp.186-191 |
| CQ, IMQ, MVE, IE (Joint) [detail] |
2022-03-09 09:45 |
Online | Online (Zoom) | Improving Weakly Supervised Instance Segmentation by Encoding Motion Information via Optical Flow Jun Ikeda, Junichiro Mori (UT) IMQ2021-15 IE2021-77 MVE2021-44 |
Weakly supervised instance segmentation is an important task that can significantly reduce the annotation cost of model ... [more] | IMQ2021-15 IE2021-77 MVE2021-44 pp.27-32 |
| SS | 2022-03-07 11:20 |
Online | Online | Trace Ablation and Fault Localization per Method Using Machine Learning Models for Automatic Classification of Test Execution Results Takuma Ikeda, Kozo Okano, Shinpei Ogata (Shinshu Univ.), Shin Nakajima (NII) SS2021-44 |
The problem to solve automatically classifying the results of test executions is called the test oracle problem. This is... [more] | SS2021-44 pp.13-18 |
| MW | 2022-03-04 11:10 |
Online | Online | Deep-Learning Based Anomaly Detection Method for Microwave Non-destructive Road Monitoring Takahide Morooka, Shouhei Kidera (Univ. of Electro-Communications) MW2021-134 |
Microwave radar is promising as large-scale and speedy non-destructive monitoring tool for aging road or tunnel because ... [more] | MW2021-134 pp.128-133 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 13:30 |
Online | Online | A Note on Automatic Diagnosis of Helicobacter Pylori Infection Based on Self-Supervised Learning and Self-Knowledge Distillation Guang Li, Ren Togo (Hokkaido Univ.), Katsuhiro Mabe (Junpukai Health Maintenance Center), Shunpei Nishida (Olympus), Yoshihiro Tomoda (Olympus Medical Systems), Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper proposes a novel method for automatic diagnosis of Helicobacter pylori (H. pylori) infection based on self-su... [more] | |
| 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 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-22 10:00 |
Online | Online | Pretext-Contrastive Learning for Self-Supervised Video Feature Learning Li Tao (UTokyo), Xueting Wang (CyberAgent, Inc.), Toshihiko Yamasaki (UTokyo) ITS2021-43 IE2021-52 |
Recently, pretext task-based methods are proposed one after another in self-supervised video feature learning. Contrasti... [more] | ITS2021-43 IE2021-52 pp.109-114 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-22 10:45 |
Online | Online | ITS2021-46 IE2021-55 |
There has been a tremendous progress in unsupervised domain adaptation (UDA), which aims to transfer knowledge acquired ... [more] | ITS2021-46 IE2021-55 pp.127-132 |
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