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All Technical Committee Conferences  (Searched in: Recent 10 Years)

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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 139  /  [Next]  
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
 Results 1 - 20 of 139  /  [Next]  
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