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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-14 16:00 |
Okinawa | Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
Study of Feature Visualization of Running Motion from RGB Videos Using Spatial Temporal Graph Convolutional Networks and Deep Metric Learning Haruya Tanaka, Chanjin Seo (Waseda Univ.), Hiroyuki Ogata (Seikei Univ.), Jun Ohya (Waseda Univ.)  IMQ2023-58 IE2023-113 MVE2023-87 |
In recent years, the running population has been increasing, and demand for coaching systems for amateur runners is expe... [more] | IMQ2023-58 IE2023-113 MVE2023-87 pp.246-251 |
| PRMU, IPSJ-CVIM, IPSJ-DCC, IPSJ-CGVI | 2023-11-17 09:20 |
Tottori | (Primary: On-site, Secondary: Online) |
A study of Recurrent Graph Convolutional Network for Sequential Prediction of 3D Human Skeleton Sequence Tomohiro Fujita, Yasutomo Kawanishi (RIKEN) PRMU2023-33 |
(To be available after the conference date) [more] | PRMU2023-33 pp.97-102 |
| CPSY, DC, IPSJ-ARC [detail] | 2023-08-04 16:00 |
Hokkaido | Hakodate Arena (Primary: On-site, Secondary: Online) |
CPSY2023-22 DC2023-22 |
(To be available after the conference date) [more] | CPSY2023-22 DC2023-22 pp.83-87 |
| PRMU, IPSJ-CVIM | 2021-03-04 14:55 |
Online | Online | Word-level sign language recognition with Multi-stream Neural Networks Focusing on Local Region Mizuki Maruyama (Osaka Pref. Univ.), Shuvozit Ghose (IIT), Katsufumi Inoue (Osaka Pref. Univ.), Partha Pratim Roy (IIT), Masakazu Iwamura, Michifumi Yoshioka (Osaka Pref. Univ.) PRMU2020-78 |
(To be available after the conference date) [more] | PRMU2020-78 pp.53-58 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2020-11-17 10:20 |
Online | Online | R-GCN Based Function Inference for An Arithmetic Circuit Yuichiro Fujishiro, Motoki Amagasaki, Masahiro Iida (Kumamoto Univ.), Hiroto Ito, Daisuke Ido (MITSUBISHI ELECTRIC ENGINEERING) VLD2020-21 ICD2020-41 DC2020-41 RECONF2020-40 |
R-GCN (Relational Graph Convolutional Network) is a convolutional neural network model for graphs consisting of nodes an... [more] | VLD2020-21 ICD2020-41 DC2020-41 RECONF2020-40 pp.60-65 |
| CPSY, DC, IPSJ-ARC [detail] | 2019-07-25 10:55 |
Hokkaido | Kitami Civic Hall | A Distributed Processing of R-GCN using Network-attached GPUs Tokio Kibata, Hiroki Matsutani (Keio Univ.) CPSY2019-24 DC2019-24 |
(To be available after the conference date) [more] | CPSY2019-24 DC2019-24 pp.103-108 |
| ET | 2019-07-06 11:00 |
Iwate | Iwate Prefectural University | Quantitative Evaluation Method for Teacher Behavior based on Behavior Pattern of Expert Teachers and Beginner Teachers Shunyu Yao, Sho Ooi, Haruo Noma (Rits Univ.) ET2019-17 |
A new teacher needs to give tuition from the first day at work. The new teacher is difficult to train tuition in an actu... [more] | ET2019-17 pp.11-16 |
| ET | 2019-03-15 11:05 |
Tokushima | Naruto University of Education | A Study on Teacher Behavior Classification based on ST-GCN for Traial Lesson Support System Sho Ooi, Yao Shunyu, Haruo Noma (Rits) ET2018-97 |
A new teacher needs to give tuition from the first day at work. People who aim for the teacher can teach at teaching pra... [more] | ET2018-97 pp.59-62 |
| IN, NS (Joint) |
2019-03-05 11:30 |
Okinawa | Okinawa Convention Center | A study on Maliciousness Measurement in Cyber Threat Intelligence Using Graph Convolutional Networks Yuta Kazato, Yoshihide Nakagawa, Yuichi Nakatani (NTT) IN2018-128 |
Cyber threat information (CTI) sharing is one of the important functions to protect end-users and services from cyber-at... [more] | IN2018-128 pp.265-270 |
| VLD, DC, CPSY, RECONF, CPM, ICD, IE, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC (Joint) [detail] |
2018-12-05 14:15 |
Hiroshima | Satellite Campus Hiroshima | Basic Evaluation of Netlist Function Inference using GCN Hiroki Oyama, Motoki Amagasaki, Masahiro Iida (kumamoto Univ.), Hiroaki Yasuda, Hiroto Ito (MITSUBISHI ELECTRIC ENGINEERING) VLD2018-44 DC2018-30 |
In recent years, Recently GCN studies on graphs has been conducted.GCN is a kind of deep learning and classifies network... [more] | VLD2018-44 DC2018-30 pp.31-36 |
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