Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
SP, IPSJ-MUS, IPSJ-SLP [detail] |
2022-06-18 13:00 |
Online |
Online |
Anomalous sound detection using multi-class classifier and reconstructor of its intermediate layer output Keita Matsumoto, Takeshi Yamada (Univ. of Tsukuba), Shoji Makino (Waseda Univ./Univ. of Tsukuba) SP2022-18 |
In recent years, there has been a growing demand for techniques to detect unknown anomalous sounds by unsupervised learn... [more] |
SP2022-18 pp.77-81 |
MI |
2022-01-27 13:54 |
Online |
Online |
[Short Paper]
Case-based Similar Image Retrieval for Pathological Images of Malignant Lymphoma Using Deep Metric Learning Noriaki Hashimoto (RIKEN), Yusuke Takagi, Hiroki Masuda (NITech), Hiroaki Miyoshi, Kei Kohno, Miharu Nagaishi, Kensaku Sato, Koichi Ohshima (Kurume Univ.), Hidekata Hontani (NITech), Ichiro Takeuchi (NITech/RIKEN) MI2021-78 |
We propose a novel method of case-based similar image retrieval for histopathological images of malignant lymphoma. We e... [more] |
MI2021-78 pp.144-145 |
CCS |
2021-11-19 14:30 |
Osaka |
Osaka Univ. (Primary: On-site, Secondary: Online) |
A Study of Deep Learning for Abnormal Waveforms in ECG Image Data Using Expert Diagnosis as a Teacher Kentaro Hashimoto, Yuichiro Yamamura (Univ of Tsukuba.), Ryota Iwatsuka (Taiyo-kai Social Welfare awachiiki iryo center), Hiroyasu Ando (Tohoku Univ./Univ of Tsukuba.) CCS2021-33 |
Artificial intelligence is expected to play a variety of roles in the medical fields. Diagnosis based on ECG readings is... [more] |
CCS2021-33 pp.89-93 |
MI, MICT [detail] |
2021-11-05 16:10 |
Online |
Online |
Classification of root resorption from dental panoramic X-ray images based on deep metric learning Kosei Tamura, Tohru Kamiya (Kyushu Institute of Technology), Masashi Oda (Kyushu Dental University), Tatsurou Tanaka (Kagoshima University), Yasuhiro Morimoto (Kyushu Dental University) MICT2021-43 MI2021-41 |
Root resorption is one of the tooth diseases. It is caused by the roots of the teeth melting when it was absorbed and ac... [more] |
MICT2021-43 MI2021-41 pp.68-71 |
SIP |
2021-08-23 13:25 |
Online |
Online |
A study on transfer learning in unsupervised anomalous sound detection based on deep metric learning considering variance of normal data Hiroki Narita, Akira Tamamori (AIT) SIP2021-28 |
In recent years, anomaly detection research in the field of computer vision has focused on methods based on transfer lea... [more] |
SIP2021-28 pp.5-10 |
EA, US, SP, SIP, IPSJ-SLP [detail] |
2021-03-04 09:00 |
Online |
Online |
Anomalous Sound Detection Using a Binary Classification Model Considering Class Centroids Ibuki Kuroyanagi, Tomiki Hayashi, Kazuya Takeda, Tomoki Toda (Nagoya Univ) EA2020-79 SIP2020-110 SP2020-44 |
In an anomalous sound detection system, it is necessary to detect unknown anomalous sounds using only normal sound data.... [more] |
EA2020-79 SIP2020-110 SP2020-44 pp.114-121 |
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2021-02-18 16:40 |
Online |
Online |
A note on improvement of image sentiment analysis based on introduction of image captioning Yun Liang, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Recently, with the popularization of social network services, the images uploaded by users have been increasing. Users t... [more] |
|
MBE, NC, NLP, CAS (Joint) [detail] |
2020-10-30 17:35 |
Online |
Online |
Hyperbolic Space Embedding for Open Set Recognition Shota Tatarai (Senshu Univ.), Yuta Ashihara (Nihon Univ/Glia Computing Co.,Ltd.), Kenji Aoki (Glia Computing Co.,Ltd.), Masahiko Osaawa (Nihon Univ./Senshu Univ.) NC2020-27 |
Many of deep learning algorithms perform well when the training and testing data are sampled from the
same class space.... [more] |
NC2020-27 pp.100-105 |
PRMU, IPSJ-CVIM |
2020-03-16 11:00 |
Kyoto |
(Cancelled but technical report was issued) |
[Short Paper]
Few-shot Character Image Generation with Deep Metric Learning Haruka Aoki, Koki Tsubota, Hikaru Ikuta, Kiyoharu Aizawa (Tokyo Univ.) PRMU2019-66 |
(To be available after the conference date) [more] |
PRMU2019-66 pp.11-12 |
PRMU, IPSJ-CVIM |
2020-03-17 10:45 |
Kyoto |
(Cancelled but technical report was issued) |
Semi-Supervised Temporal Segmentation of Industrial Operation Video based on Deep Metric Learning Daiki Kawamori, Kazuaki Nakamura, Naoko Nitta, Noboru Babaguchi (Osaka Univ.) PRMU2019-92 |
Today, cameras are often installed in many production sites for various purposes.
However, untrimmed raw videos captur... [more] |
PRMU2019-92 pp.139-144 |
BioX, CNR |
2020-03-05 14:15 |
Tokyo |
(Cancelled but technical report was issued) |
Applying Deep Metric Learning to Periocular Biometrics Tomoki Nukata, Hiroshi Yoshiura, Masatsugu Ichino (UEC) BioX2019-73 CNR2019-56 |
Periocular biometrics has been studied in recent years due to spread of surveillance cameras and smartphones. Zhao et. a... [more] |
BioX2019-73 CNR2019-56 pp.65-70 |
IA, SITE, IPSJ-IOT [detail] |
2020-03-02 14:30 |
Online |
Online |
Proposal of Anomaly Based Attack Detection System for IoT Devices Using Deep Metric Learning Tatsuya Takimoto, Hiroyuki Inaba (KIT) SITE2019-90 IA2019-68 |
In recent years, with the development of the Internet, many IoT devices are used in all aspects of life. On the other ha... [more] |
SITE2019-90 IA2019-68 pp.13-18 |
HIP |
2019-12-19 14:00 |
Miyagi |
RIEC, Tohoku University |
Mathematical Representation of Emotion by Combining Recognition and Unification Tasks Using Multimodal Deep Neural Networks Seiichi Harata, Takuto Sakuma, Shohei Kato (NITech) HIP2019-65 |
To emulate human emotions in robots, the mathematical representation of emotion is important for all components of affec... [more] |
HIP2019-65 pp.1-6 |
IBISML |
2018-11-05 15:10 |
Hokkaido |
Hokkaido Citizens Activites Center (Kaderu 2.7) |
[Poster Presentation]
Distance Metric Learning Between Graphs Based on Subgraph Tomoki Yoshida (NITech), Ichiro Takeuchi (NITech/NIMS/RIKEN), Masayuki Karasuyama (NITech/NIMS/JST) IBISML2018-64 |
A standard approach to evaluating distance between two graphs is to use common subgraphs contained in the two graphs. Fo... [more] |
IBISML2018-64 pp.151-158 |
IBISML |
2018-03-05 13:50 |
Fukuoka |
Nishijin Plaza, Kyushu University |
Metric Learning for k-Nearest Neighbor Estimation using Multiple Distance Metrics Yokuto Seki, Noboru Murata (Waseda Univ.) IBISML2017-92 |
The relationship between unstructured datasets such as graphs can be measured by multiple distance metrics.
In this pap... [more] |
IBISML2017-92 pp.15-19 |
IBISML |
2017-11-10 13:00 |
Tokyo |
Univ. of Tokyo |
Safe Screening for Large Margin Metric Learning Tomoki Yoshida (NITech), Ichiro Takeuchi (NITech/NIMS/RIKEN), Masayuki Karasuyama (NITech/NIMS/JST) IBISML2017-64 |
Large margin metric learning learns the optimal Mahalanobis distance for classification problem based on the margin maxi... [more] |
IBISML2017-64 pp.219-226 |
PRMU, IBISML, IPSJ-CVIM [detail] |
2017-09-15 16:20 |
Tokyo |
|
Ridge Regression for Improving the Accuracy of k-Nearest Neighbor Classification Yutaro Shigeto (CIT), Masashi Shimbo, Yuji Matsumoto (NAIST) PRMU2017-53 IBISML2017-25 |
This paper proposes an inexpensive way to learn an effective dissimilarity function to be used for $k$-nearest neighbor ... [more] |
PRMU2017-53 IBISML2017-25 pp.113-119 |
PRMU, IPSJ-CVIM, MVE [detail] |
2014-01-23 16:30 |
Osaka |
|
Facial Image Clustering by Evolutionary Distance Metric Learning Taishi Megano, Satoshi Ono (Kagoshima Univ.), Ken-ichi Fukui (Osaka Univ.), Kohki Ninomiya (Kagoshima Univ.), Masayuki Numao (Osaka Univ.), Shigeru Nakayama (Kagoshima Univ.) PRMU2013-101 MVE2013-42 |
In data mining and machine learning, the definition of distance between two data points substantially affects clustering... [more] |
PRMU2013-101 MVE2013-42 pp.119-124 |
IBISML, PRMU, IPSJ-CVIM [detail] |
2010-09-05 17:30 |
Fukuoka |
Fukuoka Univ. |
A Study on Feature Selection Path for High-Dimensional Local Classifiers Ichiro Takeuchi (NIT) PRMU2010-71 IBISML2010-43 |
We study feature selection and weighting problems for local-based classifier. The proposed algorithm is formulated as a ... [more] |
PRMU2010-71 IBISML2010-43 pp.105-112 |