| Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
| BCT, IEEE-BT |
2024-02-16
10:50 |
Aichi |
Nagoya International Center
(Primary: On-site, Secondary: Online) |
Adapter-Based Fine-Tuning for Multi-Task Learning Based CSI Feedback in FDD Massive MIMO Systems
Mayuko Inoue, Tomoaki Ohtsuki (Keio Univ) |
A multi-task learning-based Channel State Information (CSI) feedback has been proposed to obtain the CSI of the downlink... [more] |
BCT2024-27
pp.25-28 |
| ME |
2024-02-10
14:45 |
Online |
online |
A method of detecting pose images to support imitation learning for climbing
Yinghao Jiang, Satoshi Shimada (Nihon Univ.) |
Learning using videos is effective in improving skills in sports. Since sports enthusiasts have few opportunities to rec... [more] |
ME2024-15
pp.51-54 |
| IST |
2023-06-21
14:10 |
Tokyo |
Tokyo University of Science Morito Memorial Hall |
[Poster Presentation]
Verification of the effectiveness of multitask learning for demosaicking and white-balancing
Yuki Nakagomi, Ryoya Takeuchi, Taishi Iriyama, Takashi Komuro (Saitama Univ) |
In this study, we conducted an investigation on the learning effectiveness of jointly training demosaicking and white ba... [more] |
IST2023-25
pp.19-22 |
| IEICE-HIP, HI, VRPSY [detail] |
2023-02-22
14:45 |
Toyama |
(Primary: On-site, Secondary: Online) |
Change of color categories induced by color discrimination learning
Takehiro Nagai, Suzuha Horiuchi (Tokyo Tech) |
We examined the effects of perceptual learning of color discrimination on various color perception properties. The obser... [more] |
HI2023-3
pp.45-48 |
| MMS, ME, AIT, IEICE-IE, IEICE-ITS [detail] |
2023-02-21
11:00 |
Hokkaido |
Hokkaido Univ. |
A note on text prompt tuning in cross-modal image retrieval for a specific database
Huaying Zhang, Rintaro Yanagi, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
With the development of storage devices and the Internet, the number of users creating personal image databases has incr... [more] |
MMS2023-3 ME2023-23 AIT2023-3
pp.11-15 |
| MMS, ME, AIT, IEICE-IE, IEICE-ITS [detail] |
2023-02-21
15:30 |
Hokkaido |
Hokkaido Univ. |
Evaluating The Effectiveness of Data Augmentation for Learning TrackNetV2
Yushan Wang (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
Data augmentation has been widely used in a variety of deep learning tasks, mostly with a positive impact on the results... [more] |
MMS2023-16 ME2023-36 AIT2023-16
pp.81-84 |
| MMS, ME, AIT, IEICE-IE, IEICE-ITS [detail] |
2023-02-21
10:30 |
Hokkaido |
Hokkaido Univ. |
Improving Fashion Compatibility Prediction with Color Distortion Prediction
Ling Xiao, Toshihiko Yamasaki (UTokyo) |
Fashion compatibility prediction is suffering from the fact that the labeled dataset may become outdated quickly due to ... [more] |
|
| BCT, IEICE-SIS |
2022-10-14
10:00 |
Aomori |
Hachinohe Institute of Technology
(Primary: On-site, Secondary: Online) |
Robust Semi-Supervised Learning for Noisy Labels Using Early-learning Regularization and Weighted Loss
Ryota Higashimoto, Soh Yoshida, Mitsuji Muneyasu (Kansai Univ.) |
Training Deep Neural Networks (DNNs) on datasets with incorrect labels (label noise) is an important challenge. In the p... [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] |
|
| AIT, IIEEJ, AS, CG-ARTS |
2022-03-08
13:28 |
Online |
Online |
Style-invariant representations and frame-aware constraints for manga face image clustering
Shunta Komatsu (TUS), Ryosuke Furuta (UTokyo), Yukinobu Taniguchi (TUS), Ryota Hinami, Shonosuke Ishiwatari (Mantra) |
Character recognition in manga is an important task to improve the usability of e-comics through recommendation systems ... [more] |
AIT2022-78
pp.153-156 |
| AIT, IIEEJ, AS, CG-ARTS |
2022-03-08
15:37 |
Online |
Online |
Awareness Games: Research on entertainment games that lead to unintentional learning
-- Qualitative survey of learning methods and content --
Luna Matsukawa, Masanobu Endo (TPU) |
Serious games are developed for the purpose of learning and solving problems. In contrast, there are entertainment games... [more] |
AIT2022-80
pp.161-163 |
| AIT, IIEEJ, AS, CG-ARTS |
2022-03-08
10:30 |
Online |
Online |
Using Deep Learning to Generate Water Effects for 2D Animation
Zhaolou Liang, Masaki Abe, Taichi Watanabe (Tokyo Univ of Technology) |
In 2D animation effects, hand-drawn drawings and 3D simulations are used. Hand-drawn images have a high artistic and des... [more] |
AIT2022-112
pp.283-284 |
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-21
16:45 |
Online |
online |
A Note on Disentanglement Using Deep Generative Model Based on Variational Autoencoder
-- Introduction of Regularization Losses Based on Metrics of Disentangled Representation --
Nao Nakagawa, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In this paper, we study disentangled representation learning using a deep generative model based on Variational Autoenco... [more] |
MMS2022-19 ME2022-44 AIT2022-19
pp.97-102 |
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-21
14:25 |
Online |
online |
A Note on Visual Sentiment Prediction Based on Few-shot Learning using Knowledge Distillation
Yingrui Ye, Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
The prediction of visual sentiment can be useful to understand users' behaviors. Emotion theories underlying the sentime... [more] |
MMS2022-24 ME2022-49 AIT2022-24
pp.171-175 |
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-21
15:05 |
Online |
online |
A Note on Personalized Saliency Prediction Based on User Similarity Considering Object Information in Images
Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a personalized saliency map (PSM) prediction method using a small amount of gaze data based on user ... [more] |
MMS2022-26 ME2022-51 AIT2022-26
pp.181-186 |
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-22
13:15 |
Online |
online |
Noise-Resistant Learning for Object Detection
Jiafeng Mao, Qing Yu, Yoko Yamakata, Kiyoharu Aizawa (UTokyo) |
Supervised training of object detectors requires well-annotated large-scale datasets, whose production is extremely expe... [more] |
|
| 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 |
| ME |
2021-12-13
13:30 |
Online |
online |
Fusion model using MRI-based radiomics with deep learning feature for glioma IDH mutation predicting
Shi Xiaoyu, Zhang Xinran, Yiwamoto Yutaro (Ritsu Univ.), Jingliang Cheng, Jie Bai, Guohua Zhao (Department of Magnetic Resonance Imaging, The First Affiliated H), Chen yenwei (Ritsu Univ.) |
According to the 2016 World Health Organization classification scheme for gliomas, Isocitrate
dehydrogenase(IDH) statu... [more] |
ME2021-93
pp.21-24 |
| IIEEJ, AIT |
2021-10-27
15:40 |
Osaka |
(Primary: On-site, Secondary: Online) |
Acquisition of Human's Memory Mechanism for Video Frames
Koki Nishimoto, Kimiaki Shirahama (Kindai Univ.) |
When a video is played back, humans understand the contents while unconsciously deciding whether to memorize each frame.... [more] |
AIT2021-145
pp.17-20 |
| BCT, IEICE-SIS |
2021-10-07
15:05 |
Online |
online |
A Method for Generating Pseudo-Captured Images to Evaluate the Performance of Data Embedding Techniques to Printed Images Using Mobile Devices
Masahiro Yasuda, Mitsuji Muneyasu, Soh Yoshida (Kansai Univ.) |
A data-embedding technique to printed images has been proposed. In this technique, the embedded data is retrieved from t... [more] |
|