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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, MVE, VRSJ-SIG-MR, IPSJ-CVIM | 2024-01-25 14:40 |
Kanagawa | Keio Univ. (Hiyoshi Campus) (Kanagawa) | Efficient exploration with intrinsic motivation considering state transitions in deep reinforcement learning Kaito Ohshika, Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.)  PRMU2023-42 |
In deep reinforcement learning, learning data is collected through the interaction between the agent and the environment... [more] | PRMU2023-42 pp.14-19 |
| PRMU, IPSJ-CVIM, IPSJ-DCC, IPSJ-CGVI | 2023-11-16 16:50 |
Tottori | (Tottori, Online) (Primary: On-site, Secondary: Online) |
On Food Plant Classification from Luehdorfia Japonica Images using Multi-label Classification ABN Tsubasa Hirakawa, Takaaki Arai, Takayoshi Yamashita, Hironobu Fujiyoshi, Yuichi Oba, Hiromichi Fukui (Chubu Univ.), Masaya Yago (Tokyo Univ.) PRMU2023-27 |
Butterfly is a familiar taxon. Because of the abundance of specimens and the ease of comparison between specimens, regio... [more] | PRMU2023-27 pp.62-67 |
| PRMU, IPSJ-CVIM | 2022-03-10 16:05 |
Online | Online (Online) | Learning from AI: Proposal of interactive learning methods for learners learning from DNN embedded expertise knowledge as a teacher Kohei Hattori, Hironobu Fujiyoshi, Takayoshi Yamashita, Tsubasa Hirakawa (CHUBU) PRMU2021-70 |
(To be available after the conference date) [more] | PRMU2021-70 pp.60-65 |
| PRMU, IPSJ-CVIM | 2022-03-10 17:15 |
Online | Online (Online) | Uncertainty-Aware Interactive LiDAR Sampling for Deep Depth Completion Kensuke Taguchi, Shogo Morita, Yusuke Hayashi, Wataru Imaeda (KYOCERA), Hironobu Fujiyoshi (Chubu Univ) PRMU2021-72 |
[more] | PRMU2021-72 pp.72-77 |
| PRMU, IPSJ-CVIM | 2022-03-10 17:30 |
Online | Online (Online) | Adversarial Training: A Survey Hiroki Adachi, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2021-73 |
Adversarial training (AT) is a training method that aims to obtain a robust model for defencing the adversarial attack b... [more] | PRMU2021-73 pp.78-90 |
| PRMU | 2021-12-17 10:45 |
Online | Online (Online) | Data augmentation for domain adaptation by class consistency using soft target Wataru Imaeda (Kyocera Corp.), Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2021-49 |
Domain adaptation is a task to acquire sufficient discrimination performance between images with different domains in le... [more] | PRMU2021-49 pp.136-141 |
| PRMU | 2020-10-09 15:15 |
Online | Online (Online) | Trajectory Forecasting using Deep Learning: A Survey Horoaki Minoura, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2020-29 |
Trajectory forecasting is the technology of predicting the path along which moving objects such as a pedestrian and vehi... [more] | PRMU2020-29 pp.62-78 |
| PRMU | 2020-09-02 10:30 |
Online | Online (Online) | Image Captioning in Near-Future from Vehicle Camera images and motion information Yuki Mori, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2020-10 |
Image caption generation is a task to generate explanatory text for input images, which is used for automatic generation... [more] | PRMU2020-10 pp.13-18 |
| PRMU | 2020-09-02 15:45 |
Online | Online (Online) | Collaborative learning for generative adversarial networks Takuya Tsukahara, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2020-14 |
Generative adversarial networks (GANs) adversarially trains generative and discriminative models. And this is how to gen... [more] | PRMU2020-14 pp.41-46 |
| PRMU | 2018-12-14 10:00 |
Miyagi | (Miyagi) | Simultaneous Estimation of Facial Landmark and Attributes with Separation Multi-task Networks Ryo Matsui, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2018-82 |
(To be available after the conference date) [more] | PRMU2018-82 pp.39-44 |
| PRMU, CNR | 2018-02-20 13:20 |
Wakayama | (Wakayama) | [Special Talk]
Hironobu Fujiyoshi (Chubu Univ.), Kei Okada (Univ. of Tokyo), Koji Ehara (Toshiba Infrastructure Systems & Solutions), Gustavo Garcia (NAIST) PRMU2017-170 CNR2017-48 |
[more] | PRMU2017-170 CNR2017-48 pp.145-154 |
| PRMU | 2017-10-12 14:00 |
Kumamoto | (Kumamoto) | Self-state-aware convolutional neural network for autonomous driving Takuya Murase, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2017-77 |
[more] | PRMU2017-77 pp.85-90 |
| PRMU | 2017-10-13 09:45 |
Kumamoto | (Kumamoto) | [Survey paper] Vision-based path prediction Tsubasa Hirakawa, Takayoshi Yamashita (Chubu Univ.), Toru Tamaki (Hiroshima Univ.), Hironobu Fujiyoshi (Chubu Univ.) PRMU2017-82 |
Path prediction is a method to predict future migration pathway of an object such as pedestrian or car in a movie. Becau... [more] | PRMU2017-82 pp.109-118 |
| PRMU | 2017-10-13 14:50 |
Kumamoto | (Kumamoto) | PRMU2017-94 |
[more] | PRMU2017-94 pp.175-180 |
| PRMU, BioX | 2017-03-21 14:35 |
Aichi | (Aichi) | Noise Detection of Regression Forests with AutoEncoder Masaya Hibino (Chubu Univ.), Akisato Kimura (NTT), Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi (Chubu Univ.) BioX2016-68 PRMU2016-231 |
(To be available after the conference date) [more] | BioX2016-68 PRMU2016-231 pp.209-214 |
| PRMU, CNR | 2017-02-18 16:00 |
Hokkaido | (Hokkaido) | [Special Talk]
Hironobu Fujiyoshi (Chubu Univ.), Eiichi Matsumoto (PFN), Kei Okada (UTokyo) PRMU2016-174 CNR2016-41 |
[more] | PRMU2016-174 CNR2016-41 pp.123-129 |
| PRMU | 2016-12-16 10:00 |
Tottori | (Tottori) | [Short Paper]
Hironobu Fujiyoshi, Takayoshi Yamashita, Yuji Yamauchi (Chubu Univ.) PRMU2016-118 |
[more] | PRMU2016-118 pp.25-26 |
| PRMU | 2016-12-16 12:45 |
Tottori | (Tottori) | Hiroshi Fukui, Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi (Chubu univ.) PRMU2016-121 |
[more] | PRMU2016-121 pp.37-46 |
| PRMU | 2016-12-16 13:15 |
Tottori | (Tottori) | PRMU2016-122 |
(To be available after the conference date) [more] | PRMU2016-122 pp.47-52 |
| PRMU, BioX | 2016-03-25 15:45 |
Tokyo | (Tokyo) | Efficient Mondrian Forests by Introducing Supervised Learning Ryuei Murata (Chubu Univ.), Akisato Kimura, Yoshitaka Ushiku (NTT), Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi (Chubu Univ.) BioX2015-73 PRMU2015-196 |
Mondrian Forests is an online learning method based on framework of Random Forests. At the online learning, Mondrian For... [more] | BioX2015-73 PRMU2015-196 pp.191-196 |
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