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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-14 15:20 |
Okinawa | Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
Efficient regularizer for 4D light field image denoising based on graph-learning Rino Yoshida (TUS), Kazuya Kodama (NII), Gene Cheung (York Univ.), Takayuki Hamamoto (TUS)  IMQ2023-56 IE2023-111 MVE2023-85 |
Advanced 3D visual media as promising technology often require 4D light fields composed of dense multi-view images for a... [more] | IMQ2023-56 IE2023-111 MVE2023-85 pp.235-240 |
| IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-15 09:50 |
Okinawa | Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
IMQ2023-68 IE2023-123 MVE2023-97 |
We propose a simultaneous method of multimodal graph signal denoising and graph learning. Since sensor networks distribu... [more] | IMQ2023-68 IE2023-123 MVE2023-97 pp.301-306 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-03 15:00 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
The Generalized Denoising Autoencoder with Tweedie's Formula Yuta Aishima (NAIST), Sho Sonoda (RIKEN), Noboru Isobe (Tokyo Univ.), Kazushi Ikeda (NAIST) IBISML2023-40 |
Denoising autoencoders learn the score of the data-generating distribution, i.e., $nabla log p(x)$. However, theoretical... [more] | IBISML2023-40 pp.1-5 |
| MI | 2024-03-04 09:24 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
MI2023-64 |
(To be available after the conference date) [more] | MI2023-64 pp.103-105 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 10:10 |
Okinawa | (Primary: On-site, Secondary: Online) |
Noise-Robust Voice Conversion by Denoising Training Conditioned with Latent Variables of Speech Quality and Recording Environment Takuto Igarashi, Yuki Saito, Kentaro Seki, Shinnosuke Takamichi (UT), Ryuichi Yamamoto, Kentaro Tachibana (LY), Hiroshi Saruwatari (UT) EA2023-63 SIP2023-110 SP2023-45 |
In this paper, we propose noise-robust voice conversion by conditioning latent variables representing speech quality and... [more] | EA2023-63 SIP2023-110 SP2023-45 pp.13-18 |
| SIP, IT, RCS | 2024-01-19 13:30 |
Miyagi | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Problem of Adversarial Attacks on CNN-based Image Classifiers and Countermeasures Minoru Kuribayashi (Tohoku Univ.) IT2023-67 SIP2023-100 RCS2023-242 |
It is well-known that discriminative models based on deep learning techniques may cause misclassification if adversarial... [more] | IT2023-67 SIP2023-100 RCS2023-242 p.204 |
| EMM | 2024-01-17 10:55 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Detecting Adversarial Examples using Filtering Operation Based on JPEG-Compression-Derived Distortion Kenta Tsunomori (Okayama Univ.), Minoru Kuribayashi (Tohoku Univ.), Nobuo Funabiki (Okayama Univ.) EMM2023-87 |
Image classifiers based on convolutional neural networks are caused misclassification by adversarial perturbations. In t... [more] | EMM2023-87 pp.38-43 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 15:10 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
[Invited Talk]
-- Yuma Koizumi (Google Research) PRMU2022-87 IBISML2022-94 |
Machine learning tasks that deal with acoustic signals can be broadly classified into "recognizing sounds" and "generati... [more] | PRMU2022-87 IBISML2022-94 p.149 |
| SIS | 2023-03-02 14:40 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
QR code image dnoising netwroks based on decodability assessment Kazumitsu Takahashi, Makoto Nakashizuka (CIT) SIS2022-46 |
In this paper, an image denoising method for QR code images is proposed. The image recovery from the degraded QR code im... [more] | SIS2022-46 pp.33-36 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 13:45 |
Okinawa | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Speech and Language Research in the Google Tokyo Office Michiel Bacchiani (Google) EA2022-116 SIP2022-160 SP2022-80 |
This talk will consist of three parts. In the first part of the talk, I will reflect on some lessons learned from the ac... [more] | EA2022-116 SIP2022-160 SP2022-80 pp.239-240 |
| SIS | 2022-12-06 10:30 |
Osaka | (Primary: On-site, Secondary: Online) |
Non Local Means Based on Local Statistics for Image Sequence Denoising Ayae Takei, Mitsuhiko Meguro (Nihon Univ.) SIS2022-37 |
In this paper, we propose an extended Non Local Means for denoising noisy image sequences.Non Local Means is an image de... [more] | SIS2022-37 pp.80-85 |
| NS, ICM, CQ, NV (Joint) |
2022-11-24 14:15 |
Fukuoka | Humanities and Social Sciences Center, Fukuoka Univ. + Online (Primary: On-site, Secondary: Online) |
Research on Anomaly Detection through Analysis of Observed Traffic Using Self-Attention Yuhang Zhou, Akihiro Nakao (UTokyo) NS2022-108 |
Nowadays, threat activities have become an integral part of our network lives. The sophistication and variety of differe... [more] | NS2022-108 pp.47-52 |
| SIP | 2022-08-25 14:33 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Structured Deep Image Prior with Interscale Thresholding Jikai Li, Shogo Muramatsu (Niigata Univ.) SIP2022-55 |
This work proposes a novel image denoising technique inspired by the deep image prior (DIP) method. Our contribution is ... [more] | SIP2022-55 pp.31-36 |
| CCS, NLP | 2022-06-09 13:25 |
Osaka | (Primary: On-site, Secondary: Online) |
Learning Method for Image Denoising by Weighted Sum of Perceptual Quality Assessment Methods Takamichi Miyata (Chiba Inst. Tech.) NLP2022-2 CCS2022-2 |
Existing deep learning-based denoising methods employ mean squared error (MSE) as a loss function. As a result, the outp... [more] | NLP2022-2 CCS2022-2 pp.7-12 |
| CQ, IMQ, MVE, IE (Joint) [detail] |
2022-03-09 17:20 |
Online | Online (Zoom) | QoE Estimation for Variable Bitrate Videos Using Bio-signals Kentaro Oike, Mutsumi Suganuma, Wataru Kameyama (Waseda Univ.) CQ2021-109 |
Our previous study shows that QoE of videos can be estimated by bio-signals while watching around-10-minute variable bit... [more] | CQ2021-109 pp.49-54 |
| RCS, SR, SRW (Joint) |
2022-03-04 16:40 |
Online | Online | Denoising Method Using Deep Image Prior for Improving Accuracy of Radar Target Detection Koji Endo, Kohei Yamamoto, Tomoaki Ohtsuki (Keio Univ.) RCS2021-299 |
A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide various application... [more] | RCS2021-299 pp.241-246 |
| IPSJ-AVM, CS, IE, ITE-BCT [detail] | 2021-11-25 11:15 |
Online | Online | CS2021-62 IE2021-21 |
A light field (LF) image is composed of multi-view images acquired by slightly offset viewpoints. We propose a novel met... [more] | CS2021-62 IE2021-21 pp.13-18 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-03 10:00 |
Online | Online | A Constrained Alternating Minimization Approach to End-to-End Graph Signal Denoising Eisuke Yamagata, Shunsuke Ono (Titech) EA2020-59 SIP2020-90 SP2020-24 |
This paper proposes a denoising method for smooth graph signals observed on a graph of unknown topology. The proposed me... [more] | EA2020-59 SIP2020-90 SP2020-24 pp.1-4 |
| SIS | 2020-12-01 15:15 |
Online | Online | A Proposal of Convolutional Neural Networks detecting and removing noise for Random-Valued Impulse Noise Denoising Yukiya Fukuda (Kytutech), Ryosuke Kubota (NITUC), Hakaru Tamukoh (Kyutech) SIS2020-34 |
When digital images are transmitted, Random-Valued Impulse Noise (RVIN) may cause image degradation. RVIN is known as no... [more] | SIS2020-34 pp.35-40 |
| SeMI | 2020-01-31 13:00 |
Kagawa | Basic Evaluation of Environmental Noise Removal for Improving Alarm Sound Source Classification Performance Takeru Kadokura, Yuuki Hashizume, Yuusuke Kawakita, Hiroshi Tanaka (Kanagawa Institute of Technology) SeMI2019-114 |
The authors are studying a method to classify ringing devices with high accuracy using neural networks from indoor alarm... [more] | SeMI2019-114 pp.57-62 |
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