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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CAS, SIP, VLD, MSS | 2022-06-16 15:05 |
Aomori | Hachinohe Institute of Technology (Primary: On-site, Secondary: Online) |
Image Classification Using Vision Transformer for Compressible Encrypted Images Genki Hamano, Shoko Imaizumi (Chiba Univ.), Hitoshi Kiya (Tokyo Metropolitan Univ.)  CAS2022-8 VLD2022-8 SIP2022-39 MSS2022-8 |
In this paper, we propose an image classification method for compressible encrypted images without losing classification... [more] | CAS2022-8 VLD2022-8 SIP2022-39 MSS2022-8 pp.40-45 |
| EST | 2022-05-19 13:50 |
Tokyo | Kikai-Shinko-Kaikan Bldg. (Primary: On-site, Secondary: Online) |
Extension of the CP-EP FDTD method to the three-dimensional problem for the analysis of arbitrarily-shaped dielectric media Kazuma Takeya, Tetsuya Iwamoto, Jun Shibayama, Junji Yamauchi, Hisamatsu Nakano (Hosei Univ.) EST2022-3 |
The conventional finite-difference time-domain (FDTD) method is formulated in the Cartesian coordinate system. The stair... [more] | EST2022-3 pp.12-17 |
| PRMU, IPSJ-CVIM | 2022-03-10 09:15 |
Online | Online | Unsupervised adaptation of appearance-based gaze estimation models for domains with different label distributions. Takuru Shimoyama, Yusuke Sugano (The Univ. of Tokyo) PRMU2021-61 |
The annotation of gaze estimation is time-consuming, and it is not easy to collect training data under the exact same li... [more] | PRMU2021-61 pp.7-12 |
| 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 |
| VLD, HWS [detail] | 2022-03-08 09:55 |
Online | Online | Wafer-Level Characteristic Variation Modeling with Considering Discontinuous Effect Caused by Manufacturing Equipment Takuma Nagao (National Institute of Technology (KOSEN)), Michihiro Shintani (Nara Institute of Science and Technology), Ken'ichi Yamaguchi, Hiroshi Iwata (National Institute of Technology (KOSEN)), Tomoki Nakamura, Masuo Kajiyama, Makoto Eiki (SCK), Michiko Inoue (Nara Institute of Science and Technology) VLD2021-92 HWS2021-69 |
Statistical methods for predicting the performance of large-scale integrated circuits (LSIs) manufactured on a wafer are... [more] | VLD2021-92 HWS2021-69 pp.87-92 |
| RCS, SR, SRW (Joint) |
2022-03-04 15:20 |
Online | Online | Evaluation of the optimal position of relay station equipped with frequency converter in high frequency band Masahiro Takigawa, Ryochi Kataoka, Takeo Ohseki, Taishi Watanabe, Kosuke Yamazaki, Yoji Kishi (KDDI Research) RCS2021-291 |
In order to realize the integration of physical and cyber space (CPS: Cyber-Physical System), real-time, high-speed and ... [more] | RCS2021-291 pp.195-200 |
| MBE, NC (Joint) |
2022-03-04 14:45 |
Online | Online | EEG classification with Aligners and Adversarial Domain Adaptation for Invariant Feature Extraction and Calibration Across Subjects Tatsuhiro Shiraishi (NAIST), Reinmar Kober, Kazuaki Kawanabe (ATR) NC2021-75 |
Due to non-stationarity and inter-subject difference, conventional machine learning methods are yet to achieve a breakth... [more] | NC2021-75 pp.149-154 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-02 15:35 |
Okinawa | (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Epileptic Seizure Detection Using Active Learning with Riemannian Manifold Toshiki Orihara, Toshihisa Tanaka (TUAT) EA2021-96 SIP2021-123 SP2021-81 |
In order to realize machine learning for diagnosis, it is necessary to solve the problem that the training model is not ... [more] | EA2021-96 SIP2021-123 SP2021-81 pp.201-206 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 13:00 |
Online | Online | Domain Incremental Leaning with Adaptive Loss Functions Takumi Kawashima (UTokyo), Go Irie, Daiki Ikami (NTT), Kiyoharu Aizawa (UTokyo) ITS2021-30 IE2021-39 |
During domain incremental learning of image classification task, the distribution of images continually change, and mode... [more] | ITS2021-30 IE2021-39 pp.31-36 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 13:15 |
Online | Online | Towards Universal Deep Image Compression Koki Tsubota (UTokyo), Hiroaki Akutsu (Hitachi), Kiyoharu Aizawa (UTokyo) ITS2021-31 IE2021-40 |
In this paper, we investigate deep image compression towards universal usage. In image compression, it is desirable to b... [more] | ITS2021-31 IE2021-40 pp.37-42 |
| MI | 2022-01-26 15:00 |
Online | Online | [Special Talk]
TBA Ryoma Bise (Kyushu Univ.) MI2021-66 |
Supervised learning (e.g., deep learning) has been used for various tasks in biomedical image analysis. While supervised... [more] | MI2021-66 p.88 |
| RCS, NS (Joint) |
2021-12-16 13:00 |
Nara | Nara-ken Bunka Kaikan and Online (Primary: On-site, Secondary: Online) |
Performance Evaluation of SC-FDE Considering Radiation Pattern in Millimeter-wave and Terahertz Broadband Communications Yuto Oyamada, Shuhei Saito, Hirofumi Suganuma (Waseda Univ.), Keita Kuriyama, Yu Ono, Hayato Fukuzono, Masafumi Yoshioka (NTT), Fumiaki Maehara (Waseda Univ.) RCS2021-178 |
With the growing demand for wireless mobile services and the development of fifth- and sixth-generation mobile communica... [more] | RCS2021-178 pp.19-23 |
| MSS, CAS, IPSJ-AL [detail] | 2021-11-18 11:20 |
Online | Online | Evaluation of Accuracy in Frequency Domain Equalization with Equivalent Low-pass Model of Distributed Amplifier Taiki Okuma, Sota Ikeo, Umeda Yohtaro (Tokyo University of Science) CAS2021-42 MSS2021-22 |
In the estimation of the channel function for a circuit and the accuracy evaluation of distortion compensation using Fre... [more] | CAS2021-42 MSS2021-22 pp.28-33 |
| AP, SANE, SAT (Joint) |
2021-07-28 10:50 |
Online | Online | Wave-number domain of synthetic aperture radar with antenna pattern and distance attenuation Masanori Gocho (NICT) SANE2021-15 |
Synthetic Aperture Radar (SAR) can reconstruct two-dimensional spatial information by signal processing a series of rece... [more] | SANE2021-15 pp.1-6 |
| R | 2021-05-28 11:10 |
Online | Online | Proposal of Classifier Ensembles via Domain Knowledge for anomaly detection Masafumi Tsuyuki, Soichi Takashige, Daisuke Komaki (Hitachi) R2021-2 |
Anomaly detection models based on sensor data for preventive maintenance of facilities, need to be updated based on cont... [more] | R2021-2 pp.7-12 |
| PRMU, IPSJ-CVIM, IPSJ-NL | 2021-05-21 10:30 |
Online | Online | A Study on Domain Adaptation for Video Action Classification Utilizing Synthetic Data. Hana Isoi (Ochanomizu Univ.), Atsuko Takefusa (NII), Hidemoto Nakada (AIST), Masato Oguchi (Ochanomizu Univ.) PRMU2021-5 |
The lack of learning data is considered as one of the reasons why the classification accuracies of deep neural networks ... [more] | PRMU2021-5 pp.25-30 |
| 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 |
| SC | 2021-03-19 16:15 |
Online | Online | Implementation of Problem and Solution Sharing Service for the Elderly at Home Kazuki Unigame, Sachio Saiki, Masahide Nakamura (Kobe Univ.), Kiyoshi Yasuda (OIT) SC2020-42 |
In order to support self-help and mutual-help of the elderly at home, our research group has been working on developing ... [more] | SC2020-42 pp.55-61 |
| EMCJ, MICT (Joint) |
2021-03-05 15:35 |
Online | Online | Modeling of radio noise generated from IT equipment and home appliances Kazushi Sakuma, Satoru Shimizu, Susumu Ano, Kazunobu Serizawa, Kazuto Yano, Yoshinori Suzuki (ATR) EMCJ2020-80 |
Since the frequency and amplitude of radio noise generated by IT equipment fluctuate with time, it is difficult to expr... [more] |
EMCJ2020-80 pp.40-45 |
| IBISML | 2021-03-04 14:40 |
Online | Online | IBISML2020-59 |
In the machine learning tasks where the training data is scarce, domain adaptation (DA) is a promising methodology that ... [more] | IBISML2020-59 p.78 |
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