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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 107  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
CCS, NLP 2022-06-09
14:15
Osaka
(Primary: On-site, Secondary: Online)
Improvement of Recognition Accuracy by Sequential Execution of Unsupervised Learning and Semi-supervised Learning
Hiroki Murakami, Hidehiro Nakano (Tokyo City Univ.) NLP2022-4 CCS2022-4
In this study, we propose a sequential learning method that improves recognition accuracy by alternately utilizing the k... [more] NLP2022-4 CCS2022-4
pp.17-22
CCS, NLP 2022-06-09
14:55
Osaka
(Primary: On-site, Secondary: Online)
Basic Performance of CNNs Using Dynamic Filters Based on Octave Convolution
Kiyotaka Matono, Hidehiro Nakano (Tokyo City Univ.) NLP2022-5 CCS2022-5
The methods of using dynamic filters for convolutional neural networks (CNNs) have attracted attentions. In recent years... [more] NLP2022-5 CCS2022-5
pp.23-26
RECONF 2022-06-08
15:25
Ibaraki CCS, Univ. of Tsukuba
(Primary: On-site, Secondary: Online)
A Compact High-Speed CNN Implementation based on Redundant Computational Analysis and FPGA Acceleration
Li Qi, Li Hengyi, Meng Lin (Ritsumeikan Univ.) RECONF2022-21
Convolutional Neural Networks (CNNs) have achieved high performance and are widely used in various applications. However... [more] RECONF2022-21
pp.89-94
SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] 2022-05-20
16:40
Kumamoto Kumamoto University Kurokami Campus
(Primary: On-site, Secondary: Online)
3D Medical Image Segmentation Using 2.5D Deformable Convolutional CNN
Yuya Okumura, Kudo Hiroyuki, Takizawa Hotaka (Tsukuba Univ.) SIP2022-29 BioX2022-29 IE2022-29 MI2022-29
An effective method to improve the accuracy of 3D medical image segmentation using deep learning is to use deformable co... [more] SIP2022-29 BioX2022-29 IE2022-29 MI2022-29
pp.150-155
SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] 2022-05-20
17:00
Kumamoto Kumamoto University Kurokami Campus
(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) SIP2022-30 BioX2022-30 IE2022-30 MI2022-30
(To be available after the conference date) [more] SIP2022-30 BioX2022-30 IE2022-30 MI2022-30
pp.156-160
CQ, IMQ, MVE, IE
(Joint) [detail]
2022-03-09
10:10
Online Online (Zoom) A study on player and ball tracking in tennis videos.
Kosuke Matsumoto (Kobe univ.), Junki Tamae (iret), Nobutaka Kuroki (Kobe univ.), Kensuke Hirano (iret), Masahiro Numa (Kobe univ.) IMQ2021-16 IE2021-78 MVE2021-45
This paper proposes a player and ball tracking method in tennis videos with image processing techniques. The proposed me... [more] IMQ2021-16 IE2021-78 MVE2021-45
pp.33-38
ICTSSL, CAS 2022-01-20
15:00
Online Online On the Improvement of Recognition Accuracy of Road Sign Identification CNN using Flux and the Consideration of building RCNN
Jihang Chang, Kazuya Ozawa, Hideaki Okazaki (SIT) CAS2021-60 ICTSSL2021-37
In this report,we discuss how to improve the recognition accuracy of a neural network for recognizing traffic signs usin... [more] CAS2021-60 ICTSSL2021-37
pp.37-40
RCS, NS
(Joint)
2021-12-17
14:05
Nara Nara-ken Bunka Kaikan and Online
(Primary: On-site, Secondary: Online)
[Invited Lecture] Performance evaluation on QoS Prediction between Terminals and Access Points Using Convolutional Neural Network
Hiroya Ono, Yuki Sakaue, Satoshi Narikawa (NTT) NS2021-108 RCS2021-191
Recent mobile terminals have been able to choose from multiple connection options, and optimally accommodating them to e... [more] NS2021-108 RCS2021-191
pp.59-64(NS), pp.82-87(RCS)
DC 2021-12-10
15:25
Kagawa
(Primary: On-site, Secondary: Online)
Prediction of Train Delays at Stations Using Multiple Convolutional Neural Networks with Actual Operation Data
Tsukasa Takahashi, Takumi Fukuda, Sei Takahashi (Nihon Univ.), Hideo Nakamura (UTokyo) DC2021-61
In the metropolitan area, railroads are frequently delayed due to high congestion rates during rush hours, and many meas... [more] DC2021-61
pp.34-37
NC, MBE
(Joint)
2021-11-26
15:25
Online Online Explaining coarse visual processing in the subcortical pathway with convolutional neural networks
Chanseok Lim, Mikio Inagaki (Osaka Univ.), Takashi Shinozaki (NICT), Ichiro Fujita (Osaka Univ.) NC2021-28
The subcortical pathway for face processing conveys information rapidly but roughly to the amygdala. The fast processing... [more] NC2021-28
pp.1-6
MI, MICT [detail] 2021-11-05
10:25
Online Online Performance Improvement of Alzheimer's Disease Identification Using Cognitive Function Test Scores
Daiki Endo, Koichi Ito, Takafumi Aoki (Tohoku Univ.) MICT2021-31 MI2021-29
As the population ages, the prevalence of Alzheimer’s disease (AD) is expected to increase. AD causes progressive brain ... [more] MICT2021-31 MI2021-29
pp.17-21
EMT, IEE-EMT 2021-11-05
11:15
Online Online Quaternion convolutional neural networks for PolSAR land classification
Yuya Matsumoto, Ryo Natsuaki, Akira Hirose (UTokyo) EMT2021-43
We propose a quaternion convolutional neural network (QCNN) for Polarimetric synthetic aperture radar
(PolSAR) land cla... [more]
EMT2021-43
pp.76-81
RECONF 2021-09-10
10:20
Online Online Convolutional neural network implementations using Vitis AI
Akihiko Ushiroyama, Nobuya Watanabe, Akira Nagoya, Minoru Watanabe (Okayama Univ.) RECONF2021-19
Recently, Xilinx provides an FPGA-based Vitis AI development environment which is one of deep learning frameworks to acc... [more] RECONF2021-19
pp.13-18
CS 2021-07-16
09:40
Online Online Joint Transmit Power and Beamforming Control based on Unsupervised Machine Learning for MIMO Wireless Communication Networks
Naoto Tamada, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech) CS2021-29
In mobile communications, densely deployed cell systems are expected to improve the system capacity drastically. However... [more] CS2021-29
pp.63-68
KBSE, IPSJ-SE, SS [detail] 2021-07-08
15:15
Online Online (Zoom) A Study of Voiceprint Authentication Using Deep Learning of Image Classification
Yuki Hiroi, Mengchun Xie, Nobuo Iwasaki, Mitsutoshi Murata, Toru Mori (NIT,Wakayama College) SS2021-7 KBSE2021-19
Currently, special fraud targeting the elderly is a problem in Japan. As a countermeasure, it is possible to identify th... [more] SS2021-7 KBSE2021-19
pp.37-40
PRMU, IPSJ-CVIM, IPSJ-NL 2021-05-21
10:00
Online Online Scene Recognition of Omni-Directional Images by Patch-Based CNN
Takumi Hatogai, Takao Yamanaka (Sophia Univ.) PRMU2021-3
In this paper, a scene recognition method for omni-directional images is proposed using patch-based convolutional neural... [more] PRMU2021-3
pp.13-18
PRMU, IPSJ-CVIM, IPSJ-NL 2021-05-21
10:15
Online Online PRMU2021-4 In recent years, multi-task learning and multi-domain learning have been developed to improve the accuracy for each task... [more] PRMU2021-4
pp.19-24
CCS 2021-03-29
15:40
Online Online A 3DCNN with Reduced Parameters Using Depthwise Separable Convolution
Koki Ito, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) CCS2020-27
Convolutional Neural Networks (CNNs) have been used in various fields such as image and speech. In recent years, CNNs ha... [more] CCS2020-27
pp.37-41
PRMU, IPSJ-CVIM 2021-03-04
10:30
Online Online Learning Convolutional Neural Networks with Spatial Frequency Loss
Naoyuki Ichimura (AIST) PRMU2020-73
The pixel-wise L2 and pixel-wise L1 losses have been commonly used to measure the consistency between images in learning... [more] PRMU2020-73
pp.25-30
NC, MBE
(Joint)
2021-03-03
13:00
Online Online Hybrid Sparsity in Convolutional Neural Networks
Shoma Noguchi, Yukari Yamauchi (Nihon Univ.) NC2020-46
Convolutional neural networks (CNNs) have achieved high accuracy in areas such as image classification and object detect... [more] NC2020-46
pp.21-24
 Results 1 - 20 of 107  /  [Next]  
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