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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2024-03-03 10:05 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Post-hoc Rotational Equivariantization of Large Scale Neural Network Model and Its Application Kotaro Ogawa, Toyohiro Maki, Hidekata Hontani (NIT)  MI2023-35 |
In this study, we propose a rigid body registration method that works even for spatial deviations that involve large rot... [more] | MI2023-35 pp.19-20 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-04 11:40 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Learning and inference in the network of units with multi-dimensional internal states Akira Date, Ryosuke Yoshida (Univ. of Miyazaki) IBISML2023-51 |
We explore a network consisting of elements with multidimensional states. The element is referred to as a brick, which ... [more] |
IBISML2023-51 pp.79-85 |
| KBSE | 2024-01-24 15:40 |
Kagoshima | (Primary: On-site, Secondary: Online) |
Improvement of learning method using intermediate representation in machine learning method for code smell detection Risa Hirahara, Tomoji Kishi (Waseda Univ.) KBSE2023-64 |
In recent years，methods for detecting code smells have mainly been researched using machine learning．However，the disadva... [more] | KBSE2023-64 pp.79-84 |
| HCGSYMPO (2nd) |
2023-12-11 - 2023-12-13 |
Fukuoka | Asia pacific Import Mart (Kitakyushu) (Primary: On-site, Secondary: Online) |
Compact Emotional Space Simulating Human Percieve of Emotion Based on Crossmodal Contrastive Learning with Softlabel Seiichi Harata, Takuto Sakuma, Shohei Kato (NITech) |
This study aims to explore data-driven emotion modeling by extracting the latent space of emotions from human emotion ex... [more] | |
| NLP | 2023-11-28 10:50 |
Okinawa | Nago city commerce and industry association | Investigation of differences in latent variable space for different datasets in Sentence-BERT's image generation model Masato Izumi, Kenya Jin'no (Tokyo City Univ.) NLP2023-61 |
We have verified the degree to which sentence vectors, which are distributed representations of sentences generated by S... [more] | NLP2023-61 pp.11-14 |
| IBISML | 2023-09-08 13:25 |
Osaka | Osaka Metropolitan University (Nakamozu Campus) (Primary: On-site, Secondary: Online) |
Consideration of Negative Samples in Contrastive Learning Daiki Ishiguro, Tomoko Ozeki (Tokai Univ.) IBISML2023-28 |
Contrastive learning has achieved accuracy comparable to supervised learning. In this method, the transformed image pair... [more] | IBISML2023-28 pp.16-21 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2023-06-30 11:10 |
Okinawa | OIST Conference Center (Primary: On-site, Secondary: Online) |
On performance degradation of a method by minimizing the conditional mutual information for the out-of-distribution generalization Genki Takahashi, Toshiyuki Tanaka (Kyoto University) NC2023-15 IBISML2023-15 |
In the out-of-distribution generalization problem, the smaller the degree of change in the data generating distribution ... [more] | NC2023-15 IBISML2023-15 pp.91-97 |
| PRMU, IPSJ-CVIM | 2023-05-19 15:40 |
Aichi | (Primary: On-site, Secondary: Online) |
Object-Centric Representation Learning with Attention Mechanism Hidemoto Nakada, Hideki Asoh (AIST) PRMU2023-13 |
For object-centric representation learning, several slot-based methods, that separate objects using masks and learn the ... [more] | PRMU2023-13 pp.68-73 |
| SIS | 2023-03-02 11:00 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
Blink detection from one-dimensional face signal by using convolutional sparse dictionary learning Souichiro Maruyama, Makoto Nakashizuka (CIT) SIS2022-40 |
In this report, a blink detection method from average intensities of whole facial videos using convolutional dictionary... [more] | SIS2022-40 pp.1-4 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 14:45 |
Okinawa | (Primary: On-site, Secondary: Online) |
Personality Recognition on Dyadic Interactions with Representation Learning Nathania Nah (Tokyo Tech), Takafumi Koshinaka (YCU), Koichi Shinoda (Tokyo Tech) EA2022-117 SIP2022-161 SP2022-81 |
Personality computing explores methods of automatically measuring human traits to create a better understanding of the h... [more] | EA2022-117 SIP2022-161 SP2022-81 pp.241-246 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-21 10:30 |
Hokkaido | Hokkaido Univ. | Improving Fashion Compatibility Prediction with Color Distortion Prediction Ling Xiao, Toshihiko Yamasaki (UTokyo) ITS2022-44 IE2022-61 |
Fashion compatibility prediction is suffering from the fact that the labeled dataset may become outdated quickly due to ... [more] | ITS2022-44 IE2022-61 pp.17-18 |
| PRMU | 2022-12-15 15:30 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
Training Method for Image-based Instance Segmentation by Video-based Object-Centric Representation Learning Tomokazu Kaneko, Ryosuke Sakai, Soma Shiraishi (NEC) PRMU2022-40 |
Object-centric representation learning (OCRL) aims to separate and extract object-wise representations from an image. ... [more] |
PRMU2022-40 pp.43-48 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 16:45 |
Online | Online | A Note on Disentanglement Using Deep Generative Model Based on Variational Autoencoder
-- Introduction of Regularization Losses Based on Metrics of Disentangled Representation -- Nao Nakagawa, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In this paper, we study disentangled representation learning using a deep generative model based on Variational Autoenco... [more] | |
| CQ, CBE (Joint) |
2022-01-27 16:05 |
Ishikawa | Kanazawa（Ishikawa Pref.） (Primary: On-site, Secondary: Online) |
Proposal and evaluation of 3D-point object estimation method based on probability space representation Hiroaki Sato, Shin'ichi Arakawa, Masayuki Murata (Osaka Univ.) CQ2021-83 |
New network services are expected to emerge using real spatial information in remote areas. For the advancement of servi... [more] | CQ2021-83 pp.39-44 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-21 11:20 |
Online | Online | A Study on Elucidating Hierarchical Neural Processing of Visual and Semantic Information using Deep Learning Haruka Kawasaki (Ochadai), Satoshi Nishida (NICT), Ichiro Kobayashi (Ochadai) NC2021-32 |
As an objective to explore the neural hierarchical processing underlying the transition from visual to semantic informat... [more] | NC2021-32 pp.7-12 |
| PRMU | 2021-12-16 16:45 |
Online | Online | Verification of Cyclical Annealing for Object-Oriented Representation Learning Atsushi Kobayashi (Waseda Univ.), Hideki Tsunashima (Waseda Univ./AIST), Takehiko Ohkawa (The Univ. of Tokyo), Hiroaki Aizawa (Hiroshima Univ.), Qiu Yue, Hirokatsu Kataoka (AIST), Shigeo Morishima (Waseda Univ.) PRMU2021-39 |
Object-oriented Representation Learning is a method for obtaining images for each object and background part from an ima... [more] | PRMU2021-39 pp.83-87 |
| HCGSYMPO (2nd) |
2021-12-15 - 2021-12-17 |
Online | Online | Modality-Independent Emotion Recognition Based on Hyper-Hemispherical Embedding and Latent Representation Unification Using Multimodal Deep Neural Networks Seiichi Harata, Takuto Sakuma, Shohei Kato (NIT) |
This study aims to obtain a mathematical representation of emotions (an emotion space) common to modalities. The propos... [more] |
|
| PRMU, IPSJ-CVIM | 2021-03-05 16:10 |
Online | Online | Cross-view Non-local Neural Networks for Joint Representation Learning between First and Third Person Videos Zhehao Zhu, Yusuke Sugano, Yoichi Sato (UTokyo) PRMU2020-99 |
This paper introduces a cross-view non-local neural network to learn joint representations for understandinghuman activi... [more] | PRMU2020-99 pp.170-175 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2021-02-18 14:50 |
Online | Online | A Note on Estimation of Deteriorated Regions Based on Anomaly Detection from Rubber Material Electron Microscope Images
-- Verification of Feature Representations Extracted from Deep Learning Models -- Masanao Matsumoto, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ) |
This paper presents an anomaly detection method for estimation of deteriorated regions from rubber material electron mic... [more] | |
| PRMU | 2020-12-17 16:30 |
Online | Online | Towards Discovery of Relevant Latent Factors with Limited Data Mohit Chhabra, Quan Kong, Tomoaki Yoshinaga (Hitachi) PRMU2020-49 |
The remarkable effectiveness of neural networks on vision tasks has led to an interest in adapting neural network models... [more] | PRMU2020-49 pp.63-68 |
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