Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
EA, SIP, SP, IPSJ-SLP [detail] |
2025-03-02 11:20 |
Okinawa |
|
Speaker Verification Based on Deformable Convolutional Networks Keiya Sato, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda (NITech) EA2024-83 SIP2024-118 SP2024-24 |
In speaker verification using Deep Neural Networks, the interdependencies between frames of input features are
learned ... [more] |
EA2024-83 SIP2024-118 SP2024-24 pp.40-45 |
EA, SIP, SP, IPSJ-SLP [detail] |
2025-03-03 12:20 |
Okinawa |
|
Memory-efficient and low-computational hierarchical musical instruments classification using element selection Ryu Kato (Tokyo Metropolitan Univ.), Natsuki Ueno (Kumamoto Univ./), Nobutaka Ono (Tokyo Metropolitan Univ.), Ryo Matsuda, Kazunobu Kondo, Yu Takahashi (Yamaha Corp.) EA2024-111 SIP2024-146 SP2024-52 |
We focus on a hierarchical classification approach for musical instruments classification using machine learning to redu... [more] |
EA2024-111 SIP2024-146 SP2024-52 pp.215-220 |
EMT, IEE-EMT |
2024-11-28 10:25 |
Shizuoka |
Shizuoka Convestion & Arts Center |
Proposal of Extended Liquid Time-Constant Neural Networks to Solve Partial Differential Equations Justin Jun Wilkins, Yukihisa Suzuki (Tokyo Metropolitan Univ.) EMT2024-77 |
The purpose of this study is to develop a machine learning model using deep learning as a surrogate model for analysing ... [more] |
EMT2024-77 pp.96-101 |
EMM, ITE-ME, IE, LOIS, IEE-CMN, IPSJ-AVM [detail] |
2024-09-05 09:30 |
Hiroshima |
Hiroshima Institute of Technology (Primary: On-site, Secondary: Online) |
Adaptive multi-channel selection methods for enhancing robustness against adversarial attacks Seishu Matsui, Terumasa Aoki (Tokyo University of Technology) LOIS2024-18 IE2024-24 EMM2024-74 |
In this study, we propose a novel defense method against adversarial attacks on deep learning models, called the Adaptiv... [more] |
LOIS2024-18 IE2024-24 EMM2024-74 pp.35-40 |
IBISML, NC, IPSJ-BIO, IPSJ-MPS [detail] |
2024-06-21 16:40 |
Okinawa |
OIST |
Gradually Growing SparseNet Takumi Nakamura, Shogo Taneda, Yukari Yamauchi (NU) NC2024-26 IBISML2024-26 |
Gao Huang et al. proposed a Dense Convolutional Network (DenseNet) that takes the feature maps of all previous layers as... [more] |
NC2024-26 IBISML2024-26 pp.165-168 |
NLP, CCS |
2024-06-06 10:20 |
Fukuoka |
West Japan General Exhibition Center AIM |
The Relationship between Power Laws in Neural Representation and Image Recognition Riku Matsumoto, Yasuhiro Tsuno (Ritsumeikan Univ.) NLP2024-16 CCS2024-3 |
Recent neuroscience research has found that when examining the dimensionality of the neural state space in the primary v... [more] |
NLP2024-16 CCS2024-3 pp.8-13 |
PRMU, IPSJ-CVIM |
2024-05-16 09:45 |
Tokyo |
(Primary: On-site, Secondary: Online) |
Development and evaluation of a video segmentation model using differences between frames Sota Kawamura, Hirotada Honda, Shugo Nakamura, Takashi Sano (Toyo Univ) PRMU2024-3 |
Automatic Video Object Segmentation (AVOS) involves the extraction of target objects in videos autonomously, without hu... [more] |
PRMU2024-3 pp.13-17 |
AP |
2024-03-15 10:25 |
Fukui |
UNIVERSITY OF FUKUI (Primary: On-site, Secondary: Online) |
A Study on Path loss characteristics estimation methods considering geographical conditions for designing narrowband DR-IoT communication system Takato Ikegame, Naoki Ikeda, Motonari Imai, Tetsushi Ikegami (Meiji Univ.), Mineo Takai (Osaka Univ.), Susumu Ishihara (Shizuoka Univ.), Arata Kato, Shugo Kajita (STE) AP2023-212 |
A versatile variable-range IoT communication system using the VHF-High band, Diversified-Range IoT (DR-IoT) is being con... [more] |
AP2023-212 pp.63-67 |
DC |
2024-02-28 13:40 |
Tokyo |
Kikai-Shinko-Kaikan Bldg. |
Test Point Selection Method for Multi-Cycle BIST Using Deep Reinforcement Learning Kohei Shiotani, Tatsuya Nishikawa, Shaoqi Wei, Senling Wang, Hiroshi Kai, Yoshinobu Higami, Hiroshi Takahashi (Ehime Univ.) DC2023-98 |
Multi-cycle BIST is a test method that performs multiple captures for each scan pattern, proving effective in reducing t... [more] |
DC2023-98 pp.23-28 |
PRMU, MVE, VRSJ-SIG-MR, IPSJ-CVIM |
2024-01-26 15:46 |
Kanagawa |
Keio Univ. (Hiyoshi Campus) |
PRMU2023-48 |
In the realm of autonomous driving, end-to-end models (E2EDMs) have gained prominence due to their high predictive accur... [more] |
PRMU2023-48 pp.46-49 |
ICTSSL, CAS |
2024-01-25 11:45 |
Kanagawa |
(Primary: On-site, Secondary: Online) |
Comparison of transfer learning and fine tuning Ohata Shunsuke, Okazaki Hideaki (SIT) CAS2023-88 ICTSSL2023-41 |
This report examines the principal image recognition methods. First, we show the experimental results of image recogniti... [more] |
CAS2023-88 ICTSSL2023-41 pp.31-33 |
NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-25 09:00 |
Tokushima |
Naruto University of Education |
The Relationship Between Metrics in the Latent Variable Space and Image Classification Performance Haruki Wakasa, Kenya Jin'no (Tokyo City Univ.) NLP2023-99 MICT2023-54 MBE2023-45 |
In recent years, models based on convolutional neural networks (CNNs) have exhibited high performance in image classific... [more] |
NLP2023-99 MICT2023-54 MBE2023-45 pp.78-81 |
SeMI |
2024-01-18 13:30 |
Yamanashi |
Raki House Kaiji |
Enhancing Human Skeleton Estimation with Multi-Frame mmWave Radar Point Cloud-based Method Xintong Shi, Tomoaki Ohtsuki (Keio Univ.) SeMI2023-52 |
Millimeter-Wave (mmWave) radar-based skeleton estimation has emerged as a focal point in the realm of human motion analy... [more] |
SeMI2023-52 pp.18-21 |
NLP |
2023-11-28 13:50 |
Okinawa |
Nago city commerce and industry association |
Considerations on the distribution of latent variables in CNNs Mizuki Dai, Kenya Jin'no (Tokyo City Univ.) NLP2023-65 |
Abstract Fully comprehending the output decision mechanisms of neural networks is a critical challenge. This article foc... [more] |
NLP2023-65 pp.31-34 |
SCE |
2023-10-31 10:00 |
Miyagi |
RIEC, Tohoku Univ. (Primary: On-site, Secondary: Online) |
Design of a Modularized Circuits Library for Binary Convolutional Neural Network Accelerator using Single Flux Quantum Circuits Zeyu Han, Zongyuan Li, Yuki Yamanashi, Nobuyuki Yoshikawa (Yokohama National Univ.) SCE2023-17 |
To implement a binary neural network (BNN) based on SFQ circuits, we designed a modularized circuits library based on th... [more] |
SCE2023-17 pp.26-31 |
BioX |
2023-10-12 15:25 |
Okinawa |
Nobumoto Ohama Memorial Hall |
A Study on Ear Acoustic Personal Authentication System Using Domain Transformation for Numerical Analysis Data Masafumi Morimoto (Kansai Univ.), Shunsuke Kita (ORIST), Yoshinobu Kajikawa (Kansai Univ.) BioX2023-60 |
We propose a new personal authentication system using ear pinna transfer functions.
This proposed method is constructe... [more] |
BioX2023-60 pp.12-15 |
EMCJ, IEE-EMC, IEE-SPC |
2023-05-12 13:15 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Evaluating Transmission Efficiency Impact on Screen Reconstruction Accuracy for High-Resolution Displays Taiki Kitazawa, Yuichi Hayashi (NAIST) EMCJ2023-6 |
In TEMPEST attacks targeting high-resolution displays with divided screens, reconstructing screen information proves dif... [more] |
EMCJ2023-6 pp.1-6 |
NLP, MSS |
2023-03-17 16:05 |
Nagasaki |
(Primary: On-site, Secondary: Online) |
Lightweighting Noisy Student Semi-Supervised Learning by Applying MobileNet Yuga Morishima, Hidehiro Nakano (Tokyo City Univ.) MSS2022-108 NLP2022-153 |
Recently, Convolutional Neural Networks (CNNs) have attracted much attention in various fields such as image classificat... [more] |
MSS2022-108 NLP2022-153 pp.220-224 |
MI |
2023-03-06 13:15 |
Okinawa |
OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Improvement of Small Organ Accuracy in Multi-Organ Segmentation of Abdominal CT Images Using 2.5D Deformable Convolutional CNN Yuya Okumura, Hiroyuki Kudo, Hotaka Takizawa (Univ of Tsukuba) MI2022-80 |
In multi-organ segmentation of abdominal CT images using deep learning, small organs such as the pancreas are difficult ... [more] |
MI2022-80 pp.38-39 |
PRMU, IBISML, IPSJ-CVIM [detail] |
2023-03-02 11:15 |
Hokkaido |
Future University Hakodate (Primary: On-site, Secondary: Online) |
Dynamic Weight Scheduling for Long-tailed Visual Recognition Xinyuan Li (Ritsumeikan Univ.), Yu Wang (Hitotsubashi Univ.), Jien Kato (Ritsumeikan Univ.) PRMU2022-78 IBISML2022-85 |
For long-tailed image recognition tasks, re-weighting is effective to alleviate data imbalance by assigning higher weigh... [more] |
PRMU2022-78 IBISML2022-85 pp.107-110 |