| Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
| IEICE-ITS, IEICE-IE, ME, AIT, MMS [detail] |
2024-02-19
13:45 |
Hokkaido |
Hokkaido Univ. |
Efficient Human Pose and Shape Estimation using Decomposed Manhattan Self-Attention
Yushan Wang, Botao Zhang (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
HMR2.0, a high performance human pose and shape estimation algorithm, leverages ViT as its backbone and uses pretrained ... [more] |
MMS2024-9 ME2024-25 AIT2024-9
pp.44-48 |
| MMS, ME, AIT, IEICE-IE, IEICE-ITS [detail] |
2023-02-22
13:45 |
Hokkaido |
Hokkaido Univ. |
A Note on Age Estimation of Chum Salmon based on Characteristics of Annulus
Mikiyasu Nishiyama, Genki Suzuki, Hiroyuki Shioya (Muroran IT) |
In Hokkaido, fishing salmon, squid, and other representative regional marine products could be better, and research and ... [more] |
MMS2023-30 ME2023-50 AIT2023-30
pp.231-234 |
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-22
16:45 |
Online |
online |
A Note on Accurate Distress Classification Using Deep Learning Considering Confidence in Attention map
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a note on accurate distress classification using deep learning considering confidence in attention m... [more] |
MMS2022-33 ME2022-58 AIT2022-33
pp.371-376 |
| BCT, IEEE-BT |
2022-02-17
13:20 |
Online |
TBD |
Channel Estimation to Mitigate Channel Aging in Massive MIMO with Pilot Contamination
Hiroki Hirose, Tomoaki Ohtsuki (Keio Univ.) |
In a massive multiple-input mltiple-output (MIMO) system based on time division duplex (TDD), the channel state informat... [more] |
BCT2021-12
pp.13-16 |
| IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2021-02-18
11:35 |
Online |
Online |
A Note on Accurate Distress Image Classification of Road Structures Using Attention Map based on Text Data
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a correlation-aware attention branch network (CorABN) using multi-modal data for deterioration level... [more] |
MMS2021-4 ME2021-4 AIT2021-4
pp.17-21 |
| IEICE-MI, IEICE-IE, IEICE-SIP, IEICE-BioX, IST, ME [detail] |
2020-05-28
10:50 |
Online |
Online |
[Special Talk]
High-dimensional Signal Restoration by Convolutional Networks Driving Fusion Across Multiple Disciplines
-- Sparse Modeling and Convolutional Dictionary Learning --
Shogo Muramatsu (Niigata Univ.) |
This talk outlines a restoration process of high-dimensional signals such as image and volumetric data. With the develop... [more] |
IST2020-30 ME2020-81
p.13 |
| AIT, IIEEJ, AS, CG-ARTS |
2020-03-13
14:45 |
Tokyo |
Tokyo University of Technology
(Cancelled) |
Monocular Terrain Depth Estimation with Classification of Depth Distribution
Haruka Takahashi, Yuki Endo, Yoshihiro Kanamori, Jun Mitani (Univ. of Tsukuba) |
We propose a method for estimating depth from single-view terrain landscapes using convolutional neural networks (CNNs).... [more] |
AIT2020-66
pp.65-68 |
| 3DMT, IDY, IST [detail] |
2019-12-20
14:55 |
Aichi |
NHK Nagoya Station |
Depth from Focal Stack by Deep Neural Network
Chen Baihui, Takahashi Keita, Fujii Toshiaki (Nagoya Univ.) |
Light field theory is a heated topic in computer vision. Focal stack is one of the one of the most significant character... [more] |
IST2019-62 IDY2019-62 3DIT2019-37
pp.17-19 |
| IEICE-SIS, IPSJ-AVM, 3DMT [detail] |
2019-06-13
13:55 |
Nagasaki |
Fukue Culture Center |
Accuracy Improvement of Depth Estimation from a Single Still Image Using Feature Pyramid Network
Yudai Fukuda, Takuro Oki, Ryusuke Miyamoto (Meiji Univ.) |
Depth estimation from a single shot image have become accurate drastically after emergence of deep
neural networks that... [more] |
|
| IEICE-ITS, IEICE-IE, MMS, HI, ME, AIT [detail] |
2018-02-15
11:15 |
Hokkaido |
Hokkaido Univ. |
A Note on Recurrent Neural Network-based Tactics Estimation in Soccer Videos
Genki Suzuki, Sho Takahashi, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a method that estimates team tactics in soccer videos by a recurrent neural network.
In general, pl... [more] |
MMS2018-9 HI2018-9 ME2018-9 AIT2018-9
pp.131-135 |
| IEICE-ITS, IEICE-IE, MMS, HI, ME, AIT [detail] |
2018-02-15
15:45 |
Hokkaido |
Hokkaido Univ. |
A Note on Estimation of Users' Emotion Evoked During Listening to Music
-- Performance Improvement Based on Deep Learning Method --
Hakusyou Dan, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a method that estimates users’ emotion evoked during listening to music. In our method, we use audio... [more] |
MMS2018-21 HI2018-21 ME2018-21 AIT2018-21
pp.201-206 |
| ME, AIT, IEICE-IE [detail] |
2017-10-06
08:30 |
Nagasaki |
|
Neural network-based estimation of degree of feeling that natural objects appear in photographic images
Manami Sasaki, Koji Iwano (Tokyo City Univ.) |
Recently, it is expected that the systems for automatically adding the suitable music to a target image will be develope... [more] |
ME2017-109 AIT2017-168
pp.75-80 |
| BCT, HOKKAIDO, IEEE-BT |
2012-07-20
11:45 |
Hokkaido |
Hakodate-Simin-Kaikan |
Development of ISDB-T Interfering Station Searching Equipment
Fumiaki Minematsu, Kazuhiko Shibuya, Yoshiyuki Obara, Toru Iwasaki (NHK) |
Interferes to ISDB-T terrestrial digital TV receptions at home and relay stations by a distant terrestrial digital TV ai... [more] |
BCT2012-75
pp.61-64 |
| BCT, IEEE-BT |
2010-02-26
15:45 |
Chiba |
|
Measurement and analysis for estimating direction of arrival in ISDB-T
Sho Nabatame, Akira Nakamura, Takamasa Kuge, Kohei Ohno, Makoto Itami (TUS), Hiroki Ohta (NICT) |
An application of SFN-GF(Single Frequency Network – Gap Filler) is expected for improving the poor reception of th... [more] |
BCT2010-34
pp.13-16 |
| ME, IEICE-BS |
2008-12-18
13:25 |
Niigata |
Niigata Univ. |
Functionally layered coding for robot to robot communication
Hideki Taguchi, Masahiro Iwahashi, Tetsuya Kimura (Nagaoka Univ. of Technology) |
In this report, we propose a new video communication method for the robot vision network in which video signals are shar... [more] |
|