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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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 |
| PRMU, IPSJ-CVIM, IPSJ-DCC, IPSJ-CGVI | 2023-11-16 16:50 |
Tottori | (Primary: On-site, Secondary: Online) |
Boosting Representation Learning through Combination of Web-based Similar Image Search and Diversity-based Query Strategy Shiryu Ueno, Kunihito Kato (Gifu Univ.) PRMU2023-21 |
(To be available after the conference date) [more] | PRMU2023-21 pp.32-36 |
| 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 |
| 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] | |
| 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 | 2020-12-18 11:15 |
Online | Online | Supervised disentangled representation learning
-- Disentangling features using classifier -- Shujiro Kuroda, Toshikazu Wada (Wakayama Univ.) PRMU2020-58 |
VAE is a DNN model for unsupervised representation learning. VAE learns to extract features from the input data as laten... [more] | PRMU2020-58 pp.116-121 |
| IBISML | 2020-10-21 09:45 |
Online | Online | IBISML2020-18 |
A symbol emergence system is a multi-agent system where each autonomous agent forms internal representations through int... [more] | IBISML2020-18 pp.34-35 |
| PRMU | 2020-10-09 15:30 |
Online | Online | [Short Paper]
Analysis of DeepSets Keisuke Kanda, Seiichi Uchida (Kyushu Univ.) PRMU2020-30 |
(To be available after the conference date) [more] | PRMU2020-30 pp.79-83 |
| PRMU | 2020-09-02 16:30 |
Online | Online | Representation Learning using Video Frame Prediction and Contrastive Learning Hidemoto Nakada, Hideki Asoh (AIST) PRMU2020-17 |
The recent development in the unsupervised learning area enabled accuracy in the downstream tasks that equal the one wit... [more] | PRMU2020-17 pp.59-64 |
| PRMU, IPSJ-CVIM | 2020-03-16 10:45 |
Kyoto | (Cancelled but technical report was issued) |
Font analysis Daichi Haraguchi, Shota Harada, Brian Kenji Iwana, Seiichi Uchida (Kyushu Univ.) PRMU2019-65 |
We conducted a font analysis experiment. [more] | PRMU2019-65 pp.5-10 |
| IBISML | 2020-03-11 09:20 |
Kyoto | Kyoto University (Cancelled but technical report was issued) |
Subspace Representation for Graphs Junki Ishikawa, Hiroaki Shiokawa, Kazuhiro Fukui (Tsukuba Univ.) IBISML2019-40 |
In this research, we discuss a representation learning for graph analysis, where a graph is represented by a low dimensi... [more] | IBISML2019-40 pp.51-57 |
| AI | 2020-02-14 16:50 |
Shimane | Izumo Campus, Shimane University | A Data Fusion Method Assuming Latent Proxy Variables for Target Variables Yoshihide Nishio, Yasuo Tanida (Synergy Marketing) AI2019-52 |
We propose an analysis method that enables cross-domain prediction and interpretation of consumer behavior, and maintain... [more] | AI2019-52 pp.55-60 |
| NLC, IPSJ-NL, SP, IPSJ-SLP (Joint) [detail] |
2019-12-06 16:25 |
Tokyo | NHK Science & Technology Research Labs. | An evaluation of representation learning using phoneme posteriorgrams and data augmentation in speech emotion recognition Shintaro Okada (Nagoya Univ.), Atsushi Ando (Nagoya Univ./NTT), Tomoki Toda (Nagoya Univ.) SP2019-43 |
This paper presents a new speech emotion recognition method based on representation learning and data augmentation. To ... [more] |
SP2019-43 pp.91-96 |
| NLC, IPSJ-DC | 2019-09-27 17:25 |
Tokyo | Future Corporation | Caputuring the correlation between consumers' preferences among different domains from E-commerce review data Gaia Suzuki, Masanao Ochi, Ichiro Sakata (The Univ. of Tokyo) NLC2019-15 |
Segmentation is essential for strategical marketing, but it is considered difficult to both divide market needs among di... [more] | NLC2019-15 pp.35-40 |
| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 15:55 |
Okayama | PRMU2019-13 MI2019-32 |
(To be available after the conference date) [more] | PRMU2019-13 MI2019-32 pp.9-13 |
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