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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 09:50 |
Okinawa | (Primary: On-site, Secondary: Online) |
Domain Adaptation for Improving End-to-end ASR Performance of Classroom Speech with Variable Recording Condition Raufun Nahar, Rino Suzuki, Atsuhiko Kai (Shizuoka Univ.)  EA2022-101 SIP2022-145 SP2022-65 |
Automatic speech recognition (ASR) of real-world speech recorded in real environment has been a challenge in the field o... [more] | EA2022-101 SIP2022-145 SP2022-65 pp.153-158 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [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] | |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-22 11:00 |
Hokkaido | Hokkaido Univ. | Discussion and user study of displaying 360-degree video that follows RoI Yuuki Sawabe (UTokyo), Satoshi Ikehata (NII), Kiyoharu Aizawa (UTokyo) ITS2022-63 IE2022-80 |
Although 360° video images contain information in all directions, the user's viewing angle is limited, resulting in over... [more] | ITS2022-63 IE2022-80 pp.118-123 |
| EA, US (Joint) |
2022-12-22 16:50 |
Hiroshima | Satellite Campus Hiroshima | [Poster Presentation]
Data augmentation method for machine learning on speech data Tsubasa Maruyama (Tokyo Tech), Tsutomu Ikegami (AIST), Toshio Endo (Tokyo Tech), Takahiro Hirofuchi (AIST) EA2022-68 |
In machine learning, data augmentation is a method to enhance the number and diversity of data by adding transformations... [more] | EA2022-68 pp.42-48 |
| PRMU | 2022-12-16 14:40 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
Data Augmentation Shumpei Takezaki (Kyushu Univ.), Kiyohito Tanaka (Kyoto Second Red Cross Hospital), Seiichi Uchida, Takeaki Kadota (Kyushu Univ.) PRMU2022-50 |
Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of i... [more] | PRMU2022-50 pp.95-99 |
| CCS | 2022-11-17 14:55 |
Mie | (Primary: On-site, Secondary: Online) |
Long-term modeling of financial machine learning with multiple time scales Kazuki Amagai (Ibaraki Univ.), Riku Tanaka (Daiwa Asset Management), Tomoya Suzuki (Ibaraki Univ.) CCS2022-47 |
In asset management businesses such as operating mutual funds, medium or long-term investments are common in terms of op... [more] | CCS2022-47 pp.19-24 |
| CCS | 2022-11-18 15:20 |
Mie | (Primary: On-site, Secondary: Online) |
Multi-domain translation from few data by CycleGAN applying data augmentation Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-59 |
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] | CCS2022-59 pp.81-84 |
| PRMU | 2022-09-14 10:30 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Performance Evaluation of Data Augmentation Using Face Parsing for Improving Face Recognition Hiroya Kawai, Koichi Ito (Tohoku Univ.), Hwann-Tzong Chen (NHTU), Takafumi Aoki (Tohoku Univ.) PRMU2022-12 |
Face recognition is one of the most promising methods to recognize individuals. Since the recognition accuracy is degrad... [more] | PRMU2022-12 pp.13-18 |
| PRMU | 2022-09-14 15:45 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Human Pose Transfer with Reduced Color Transfer by Occlusion for Person Re-Identification Masaki Kishibe, Toshikazu Wada (Wakayama Univ.) PRMU2022-15 |
Human pose transfer is the task that transforms a person image from the source pose to a given target pose, and is usefu... [more] | PRMU2022-15 pp.31-36 |
| PRMU | 2022-09-14 16:15 |
Kanagawa | (Primary: On-site, Secondary: Online) |
Data Augmentation with Style Transfer for Fossil Image Segmentation Akihiro Waza (Osaka Metropolitan Univ.), Yuya Inamura (Osaka Prefecture Univ.), Katsufumi Inoue, Michifumi Yoshioka, Toshihiro Yamada (Osaka Metropolitan Univ.) PRMU2022-17 |
Fossils are extremely important materials in evolutionary biology and earth science. However, it is necessary to have sp... [more] | PRMU2022-17 pp.43-48 |
| IN, CCS (Joint) |
2022-08-04 10:00 |
Hokkaido | Hokkaido University(Centennial Hall) (Primary: On-site, Secondary: Online) |
Investigation on Applying Data Augmentation to CycleGAN Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-26 |
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] | CCS2022-26 pp.1-5 |
| IA, ICSS | 2022-06-23 14:30 |
Nagasaki | Univ. of Nagasaki (Primary: On-site, Secondary: Online) |
Discussion about improving a detection accuracy of malware variants using time series differences in latent representation. Atsushi Shinoda, Hajime Shimada, Yukiko Yamaguti (Nagoya Univ.), Hirokazu Hasegawa (NII) IA2022-4 ICSS2022-4 |
Today, computers are used for various purposes to support people's daily lives. Therefore, the existence of malware that... [more] | IA2022-4 ICSS2022-4 pp.19-24 |
| PRMU, IPSJ-CVIM | 2022-03-11 14:30 |
Online | Online | Background Mixup Data Augmentation for Hand and Object-in-Contact Detection Koya Tango, Takehiko Ohkawa, Ryosuke Furuta, Yoichi Sato (UTokyo) PRMU2021-82 |
Detecting the position of human hands and an object-in-contact from an image is vital for understanding a user’s actions... [more] | PRMU2021-82 pp.139-144 |
| CPSY, DC, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC [detail] | 2022-03-10 10:30 |
Online | Online | A Don't Care Filling Method of Control Signals for Concurrent Logical Fault Testing Haofeng Xu, Toshinori Hosokawa, Hiroshi Yamazaki, Masayuki Arai (Nihon Univ), Masayoshi Yoshimura (KSU) CPSY2021-56 DC2021-90 |
In recent years, with the increase in test cost for VLSIs, it has been important to reduce the number of test patterns. ... [more] | CPSY2021-56 DC2021-90 pp.67-72 |
| SeMI, IPSJ-MBL, IPSJ-UBI | 2022-03-08 14:45 |
Online | Online | Evaluation of Data Augmentation Methods Considering Occlusion Region for 3D Point Cloud Classification Shiori Maki, Kenji Kanai, Shota Hirose, Heming Sun, Jiro Katto (Waseda Univ.) SeMI2021-91 |
In recent years, research of point cloud classification using deep learning has been improved. In this paper, we propose... [more] | SeMI2021-91 pp.47-52 |
| PRMU | 2021-12-17 10:30 |
Online | Online | Data Augmentation to Robust Deep Learning-Based Lesion Classification for CT Image with Different Imaging Conditions Nobuhiro Miyazaki, Hiroaki Takebe, Takayuki Baba (FUJITSU), Hiroaki Terada, Toru Higaki, Kazuo Awai (Hiroshima Univ.), Masahiko Shimada (Fujitsu Japan) PRMU2021-48 |
In this paper, we propose a data augmentation to robust DL (deep learning)-based lesion classification for CT image with... [more] | PRMU2021-48 pp.130-135 |
| PRMU | 2021-10-09 10:45 |
Online | Online | Moving Scene Text Detection Using Synthetic Scene Text Video for Training Zhiyuan Xie, Hideaki Goto, Takuo Suganuma (Tohoku Univ.) PRMU2021-21 |
In computer vision areas, scene text is valuable information for applications including scene understanding, autopilot, ... [more] | PRMU2021-21 pp.28-33 |
| NLC | 2021-09-16 10:00 |
Online | Online | A causal relation extraction among distant texts using deep learning Pengju Gao, Tomohiro Yamasaki, Masahiro Ito (TOSHIBA) NLC2021-8 |
Most of the Existing methods for causal relationship extraction utilize patterns such as clue expressions, but it is dif... [more] | NLC2021-8 pp.11-16 |
| 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 |
| PRMU, IPSJ-CVIM | 2021-03-05 14:10 |
Online | Online | A Consideration on Suspicious Object Detection by Mixup and Improved U-Net Naruki Kanno, Wataru Kameyama, Toshio Sato, Yutaka Katsuyama, Takuro Sato (Waseda Univ.) PRMU2020-90 |
In this paper, on suspicious object detection by using semantic segmentation, we study the effectiveness of Mixup data a... [more] | PRMU2020-90 pp.121-126 |
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