| Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
| IIEEJ, AIT |
2022-10-30
14:50 |
Online |
on line |
[Invited Talk]
Advances in Image Editing Techniques with Deep Learning
Satoshi Iizuka (Univ. of Tsukuba) |
In this presentation, I will introduce how image editing techniques have been developed through deep learning. The metho... [more] |
AIT2022-175
p.13 |
| IEICE-SIP, IEICE-BioX, IEICE-IE, IEICE-MI, IST, ME [detail] |
2022-05-19
16:10 |
Kumamoto |
Kumamoto University
(Primary: On-site, Secondary: Online) |
[Invited Talk]
Image and Video Restoration with Deep Learning
Satoshi Iizuka (Univ. of Tsukuba) |
In this talk, I will introduce techniques for restoring black-and-white images and videos with high accuracy using deep ... [more] |
|
| IEICE-SIP, IEICE-BioX, IEICE-IE, IEICE-MI, IST, ME [detail] |
2022-05-20
16:40 |
Kumamoto |
Kumamoto University
(Primary: On-site, Secondary: Online) |
3D Medical Image Segmentation Using 2.5D Deformable Convolutional CNN
Yuya Okumura, Kudo Hiroyuki, Takizawa Hotaka (Tsukuba Univ.) |
An effective method to improve the accuracy of 3D medical image segmentation using deep learning is to use deformable co... [more] |
|
| IEICE-SIP, IEICE-BioX, IEICE-IE, IEICE-MI, IST, ME [detail] |
2022-05-20
17:00 |
Kumamoto |
Kumamoto University
(Primary: On-site, Secondary: Online) |
Deformable registration of 3D medical images with Deep Residual UNet
Taiga Nakamura, Yuki Sato, Hiroyuki Kudo, Hotaka Takizawa (Univ. of Tsukuba) |
(To be available after the conference date) [more] |
|
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-21
13:15 |
Online |
online |
Towards Universal Deep Image Compression
Koki Tsubota (UTokyo), Hiroaki Akutsu (Hitachi), Kiyoharu Aizawa (UTokyo) |
In this paper, we investigate deep image compression towards universal usage. In image compression, it is desirable to b... [more] |
|
| BCT, IEICE-SIS |
2021-10-08
10:00 |
Online |
online |
[Tutorial Lecture]
The Past and The Future of Explainable AI Techniques
Yoshitaka Kameya (Meijo Univ.) |
Machine learning models of high predictive performance, such as deep neural networks and ensemble models, now play a cen... [more] |
|
| BCT, IEEE-BT |
2021-09-03
13:35 |
Online |
Online |
Convolutional Radio Modulation Recognition Networks with Attention Models in Wireless Systems
Haohui Jia, Na Chen, Minoru Okada (NAIST) |
In modern wireless systems, deep learning (DL) shows promising performance for wireless signal processing. DL model driv... [more] |
BCT2021-35
pp.1-4 |
| HI, IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2020-02-27
14:00 |
Hokkaido |
Hokkaido Univ.
(Cancelled) |
An Image Transformation Network for Privacy-Preserving Deep Neural Networks
Hiroki Ito, Yuma Kinoshita, Hitoshi Kiya (Tokyo Metro. Univ.) |
We propose an image transformation network to generate visually-protected images for privacy-preserving deep neural netw... [more] |
|
| 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
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 |
| IEICE-ITS, IEICE-IE, MMS, HI, ME, AIT [detail] |
2018-02-16
13:00 |
Hokkaido |
Hokkaido Univ. |
Study of Multi-Scale Residual Network for Image-to-Image Translation
Rei Endo, Yoshihiko Kawai, Takahiro Mochizuki (NHK) |
In recent years, deep neural networks have widely studied in various fields such as object recognition, scene classifica... [more] |
MMS2018-35 HI2018-35 ME2018-35 AIT2018-35
pp.319-322 |