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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SeMI, SeMI (Joint) |
2023-01-20 10:20 |
Tokushima | Naruto grand hotel (Primary: On-site, Secondary: Online) |
Arterial Blood Pressure Waveform Estimation from Photoplethysmogram under Inter-subject Paradigm by U-Net and Domain Adversarial Training Rikuto Yoshizawa, Kohei Yamamoto, Tomoaki Ohtsuki (Keio)  SeMI2022-96 |
Blood pressure (BP) estimation methods using photoplethysmogram (PPG) signals based on deep learning models have been ac... [more] | SeMI2022-96 pp.113-118 |
| MI | 2022-07-08 16:00 |
Hokkaido | (Primary: On-site, Secondary: Online) |
[Short Paper]
Unsupervised Domain Adaptation for Liver Tumor Detection in Multi-Phase CT images Using Adversarial Learning with Maximum Square Loss Rahul Kumar Jain (Ritsumeikan Univ.), Takahiro Sato, Taro Watasue, Tomohiro Nakagawa (tiwaki), Yutaro Iwamoto (Ritsumeikan Univ.), Xianhua Han (Yamaguchi Univ.), Lanfen Lin, Hongjie Hu (Zhejiang Univ.), Yen-Wei Chen (Ritsumeikan Univ.) MI2022-37 |
Liver tumor detection in multi-phase CT images is essential in computer-aided diagnosis. Deep learning has been widely ... [more] | MI2022-37 pp.22-23 |
| PRMU, IPSJ-CVIM | 2022-03-11 14:45 |
Online | Online | Hand Segmentation in Egocentric Videos by Combining UMA and MCD Kenichi Suzuki, Katsufumi Inoue, Michifumi Yoshioka (Osaka Prefecture Univ.) PRMU2021-83 |
Domain shift in the egocentric video analysis is caused by the difference between shooting environment of training and t... [more] | PRMU2021-83 pp.145-150 |
| PRMU | 2021-12-16 14:45 |
Online | Online | Unsupervised Logo Detection Using Adversarial Learning from Synthetic to Real Images Rahul Kumar Jain (Ritsumeikan Univ.), Takahiro Sato, Taro Watasue, Tomohiro Nakagawa (tiwaki), Yutaro Iwamoto (Ritsumeikan Univ.), Xiang Ruan (tiwaki), Yen-Wei Chen (Ritsumeikan Univ.) PRMU2021-31 |
Most of the existing deep learning based logo detection methods typically use a large amount of annotated training data,... [more] | PRMU2021-31 pp.43-44 |
| CCS | 2021-03-29 16:05 |
Online | Online | IMAS-GAN: Unsupervised Domain Translation without Cycle Consistency Masashi Okada, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) CCS2020-28 |
CycleGAN realizes the translation between domains without using pair data. However, the configuration of two GANs and th... [more] | CCS2020-28 pp.42-47 |
| NLC | 2020-09-10 15:25 |
Online | Online | Unsupervised Domain Adaptation for Dialogue Sequence Labeling
-- Application to Contact Center Tasks -- Shota Orihashi, Naoki Makishima, Mana Ihori, Akihiko Takashima, Tomohiro Tanaka, Ryo Masumura (NTT) NLC2020-8 |
This paper presents an unsupervised domain adaptation for utterance-level sequence labeling of conversation in a contact... [more] | NLC2020-8 pp.34-39 |
| MI | 2020-01-30 13:25 |
Okinawa | OKINAWAKEN SEINENKAIKAN | Extracting and Visualization of Essential Features for Staining Translation of Pathological Images Ryoichi Koga, Noriaki Hashimoto, Tatsuya Yokota (NIT), Masato Nakaguro, Kei Kohno, Shigeo Nakamura (NUI), Ichiro Takeuchi, Hidekata Hontani (NIT) MI2019-116 |
In this manuscript, we propose a method for stain translation of pathology images. When one constructs a computer aided ... [more] | MI2019-116 pp.215-218 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Kei Yonekawa, Hao Niu, Mori Kurokawa, Arei Kobayashi (KDDI Research) IBISML2018-103 |
(Advance abstract in Japanese is available) [more] | IBISML2018-103 pp.435-440 |
| SP | 2018-08-27 11:35 |
Kyoto | Kyoto Univ. | [Poster Presentation]
An Experimental Study on Transforming the Emotion in Speech using GAN Kenji Yasuda, Ryohei Orihara, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga (UEC) SP2018-26 |
In domain transfer task deep learning has made it possible to generate more natural and highly accurate output. Especial... [more] | SP2018-26 pp.19-22 |
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