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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 21  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2020-02-28
16:35
Hokkaido Hokkaido Univ. A Note on Event Classification in Soccer Videos Using Bidirectional LSTM
Tomoki Haruyama, Sho Takahashi, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
This paper presents a method for event classification using Bidirectional LSTM (Bi-LSTM) in soccer videos. In soccer com... [more]
NLC 2020-02-17
10:20
Tokyo Seikei University Analysing emergency care team leaders' eye gaze for understanding non-verbal behaviours in emergency care interaction
Keiko Tsuchiya, Akira Taneichi (YCU), Kyota Nakamura (YCU Medical Cetnre), Takuma Sakai (YKH), Takeru Abe (YCU Medical Cetnre), Takeshi Saitoh (Kyutech) NLC2019-42
In emergency care interaction, a team leader collaborates with his members to safely perform medical procedures. This pr... [more] NLC2019-42
pp.33-36
NLP, NC
(Joint)
2020-01-25
09:30
Okinawa Miyakojima Marine Terminal On univariate continuous multimodal analysis and discrete multimodal analysis (2)
Hideo Kanemitsu, Konno Hideaki (Hokkaido Univ. of Edu.) NLP2019-101
The (continuous) multimodal function of continuous variables defined by the authors focuses on the minimum (local) value... [more] NLP2019-101
pp.83-88
HCS 2019-08-23
16:45
Osaka Jikei Institute Collection and Analysis of Human-System Multimodal Dialogue Data with Subjective Ratings
Kazunori Komatani (Osaka Univ.), Shogo Okada (JAIST) HCS2019-33
Dialogue systems need to capture not only the verbal content that the user explicitly speaks but also the subtle behavio... [more] HCS2019-33
pp.21-26
NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] 2019-06-17
16:15
Okinawa Okinawa Institute of Science and Technology Imputation of Missing Time-Series Multimodal Data with Variational Autoencoder
Ryoichi Kojima, Shinya Wada, Kiyohito Yoshihara (KDDI Research) IBISML2019-8
Data is often missing and that results in negative effects on subsequent data analysis and creating machine learning mod... [more] IBISML2019-8
pp.51-55
MVE, ITE-HI, ITE-SIP [detail] 2019-06-10
10:30
Tokyo   Impression Prediction of Oral Presentation Using LSTM with Dot-product Attention Mechanism
Shengzhou Yi, Xueting Wang, Toshihiko Yamasaki (UTokyo) MVE2019-1
For automatically evaluating oral presentation, we propose an end-to-end system to predict audience’s impression on spee... [more] MVE2019-1
pp.1-6
SP 2019-01-27
11:30
Ishikawa Kanazawa-Harmonie Multimodal Data Augmentation for Visual Speech Recognition using Deep Canonical Correlation Analysis
Masaki Shimonishi, Satoshi Tamura, Satoru Hayamizu (Gifu University) SP2018-60
This paper proposes ta new data augmentation strategy for deep learning, in which feature vectors in one modality can be... [more] SP2018-60
pp.41-45
PRMU 2018-12-14
14:10
Miyagi   What Affects Visual Ad Clicks in Social Media Platforms?
Yuki Iwazaki, Kota Yamaguchi (CyberAgent, Inc.) PRMU2018-88
In the online advertisement, high-quality prediction on click-through rate (CTR) is crucial for delivering the optimal a... [more] PRMU2018-88
pp.67-72
HCS 2017-08-21
14:10
Tokyo Seikei University Meeting Extracts for Group Discussions using Multimodal Convolutional Neural Networks
Fumio Nihei, Yukiko Nakano, Yutaka Takase (Seikei Univ.) HCS2017-57
With the goal of extracting meeting minutes from group discussion corpus, this study proposes multimodal fusion models b... [more] HCS2017-57
pp.55-59
PRMU, IE, MI, SIP 2017-05-25
14:10
Aichi   Dominant Line Extraction in Multimodal GIS Data
Tomohiro Nishikawa, Yuichi Tanaka (TUAT) SIP2017-8 IE2017-8 PRMU2017-8 MI2017-8
In this report, we propose a multiple line extraction method from multimodal GIS data distributed in high dimensional sp... [more] SIP2017-8 IE2017-8 PRMU2017-8 MI2017-8
pp.43-48
SP, IPSJ-SLP, NLC, IPSJ-NL
(Joint) [detail]
2016-12-20
10:20
Tokyo NTT Musashino R&D Constructing a Japanese multimodal corpus from emotional monologues and dialogues
Nurul Lubis (NAIST), Randy Gomez (HRI), Sakriani Sakti (NAIST), Keisuke Nakamura (HRI), Koichiro Yoshino, Satoshi Nakamura (NAIST), Kazuhiro Nakadai (HRI) SP2016-51
To fully incorporate emotion into human-computer interaction, rich sets of labeled emotional data is prerequisite. Howev... [more] SP2016-51
pp.9-10
WIT, HI-SIGACI 2016-12-14
16:10
Tokyo AIST Tokyo Waterfront Training effects of automated social skills trainer for users of autism spectrum disorders
Hiroki Tanaka (NAIST), Hideki Negoro (Nara University of Education), Hidemi Iwasaka (Heartland Shigisan), Satoshi Nakamura (NAIST) WIT2016-52
We have attempted to automate one or several parts of social skills training through multimodal human-computer interacti... [more] WIT2016-52
pp.39-44
IBISML 2016-11-17
14:00
Kyoto Kyoto Univ. [Poster Presentation] Analysis of Multimodal Deep Neural Networks -- Towards the elucidation of the modality integration mechanism --
Yoh-ichi Mototake, Takashi Ikegami (unit of Tokyo) IBISML2016-97
With the rapid development of information technology in recent years,
several machine learning algorithms that integra... [more]
IBISML2016-97
pp.369-373
PRMU, IPSJ-CVIM, MVE [detail] 2015-01-22
15:00
Nara   Automatic learning and recognition of unknown object by image search and keyword search
Shunichiro Mizuno, Osamu Hasegawa (Tokyo Tech) PRMU2014-96 MVE2014-58
We should teach the feature to the computer system to recognize the real world object. This is very laborious work. In o... [more] PRMU2014-96 MVE2014-58
pp.105-110
PRMU 2014-03-13
13:15
Tokyo   [Special Talk] Trial Realization of Associative Search for Media Understanding
Miki Haseyama (Hokkaido Univ.) PRMU2013-180
This paper presents a new associative search system for enhancing serendipity which collaboratively use several unstruct... [more] PRMU2013-180
pp.73-77
SP 2010-06-17
16:15
Fukuoka Kyushu University Decision Fusion using Boosting Method for Multi-Modal Voice Activity Detection
Shin'ichi Takeuchi, Takashi Hashiba, Satoshi Tamura, Satoru Hayamizu (Gifu Univ.) SP2010-26
In this paper, we propose a multi-modal voice activity detection system
(VAD) that uses audio and visual information. ... [more]
SP2010-26
pp.25-30
PRMU, SP, MVE, CQ 2010-01-22
14:50
Kyoto Kyoto Univ. Multimodal speech recognition using multimodal voice activity detection
Satoshi Tamura, Masato Ishikawa, Takashi Hashiba, Shin'ichi Takeuchi, Satoru Hayamizu (Gifu Univ.) CQ2009-105 PRMU2009-204 SP2009-145 MVE2009-127
Audio-Visual Automatic Speech Recognition (AVASR) has been developed to enhance the robustness in noisy environments, us... [more] CQ2009-105 PRMU2009-204 SP2009-145 MVE2009-127
pp.345-350
SP, NLC 2008-12-10
16:10
Tokyo Waseda Univ. Driver's irritation detection using speech recognition results
Lucas Malta, Chiyomi Miyajima, Akira Ozaki, Norihide Kitaoka, Kazuya Takeda (Nagoya Univ.) NLC2008-65 SP2008-120
In this work we present our efforts towards the multi-modal estimation of a driver's affective state under naturalistic ... [more] NLC2008-65 SP2008-120
pp.245-248
CQ 2007-07-13
14:00
Hokkaido Shiretoko Prince Hotel Kazenamiki Implementation and basic evaluation of handover management scheme supporting multiple connections in multimodal environment
Kazuya Tsukamoto, Yuki Nakano, Takeshi Yamaguchi (KIT), Shigeru Kashihara (NAIST), Yuji Oie (KIT) CQ2007-33
With the wide spread of various data link technologies, in the near future, mobile nodes (MNs) will automatically select... [more] CQ2007-33
pp.113-118
SP 2007-05-31
11:30
Kyoto ATR A study on multimodal speech recognition for spoken dialogue systems
Shunsuke Takayama, Toshihide Matsuo, Koji Iwano, Sadaoki Furui (Tokyo Tech) SP2007-4
This paper describes speaker-independent multimodal speech recognition toward constructing multimodal spoken dialogue sy... [more] SP2007-4
pp.19-24
 Results 1 - 20 of 21  /  [Next]  
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