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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
HCGSYMPO
(2nd)
2020-12-15
- 2020-12-17
Online Online Objective Prediction of Social Skills Level for Automated Social Skills Training Using Multimodal Information
Takeshi Saga, Hiroki Tanaka (NAIST), Hidemi Iwasaka (Nara Med. Univ.), Satoshi Nakamura (NAIST)
Although Social Skills Training is a well-known effective method to obtain appropriate social skills during daily commun... [more]
NLC 2020-02-17
09:30
Tokyo Seikei University Group performance modeling using verbal and nonverbal information
Go Miura, Shogo Okada (JAIST) NLC2019-40
This paper presented computational analysis for predicting the group performance which is defined by
"product dimension... [more]
NLC2019-40
pp.21-26
IMQ 2019-12-20
16:45
Tokyo   Effect of Visual and Auditory Adaptation on Material Perception
Takumi Nakamura, Kuangzhe Xu, Toshihiko Matsuka, Keita Hirai (Chiba Univ) IMQ2019-11
In this paper, we investigated the effects of visual and auditory adaptation on material perception. The target material... [more] IMQ2019-11
pp.9-13
NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] 2019-06-17
13:00
Okinawa Okinawa Institute of Science and Technology Reliability Assessment by Bayesian Deep Learning for Image-Caption Retrieval Task
Kenta Hama, Takashi Matsubara, Kuniaki Uehara (Kobe Univ.) IBISML2019-1
Following the development of black-box machine learning algorithms, the practical demand of the re- liability assessment... [more] IBISML2019-1
pp.1-8
IBISML 2018-11-05
15:10
Hokkaido Hokkaido Citizens Activites Center (Kaderu 2.7) [Poster Presentation] Regularized Modal Regression via Iteratively Reweighted Least-Squares
Ryoya Yamasaki, Toshiyuki Tanaka (Kyoto Univ.) IBISML2018-88
In modal regression, iterative methods that are based on the modal EM (MEM) and those based on the iteratively reweighte... [more] IBISML2018-88
pp.329-335
IBISML 2017-11-09
13:00
Tokyo Univ. of Tokyo Efficient modal regression using gradient ascent and descent method
Ryoya Yamasaki, Toshiyuki Tanaka (Kyoto Univ.) IBISML2017-57
Nonparametric modal regression is a method for regression analysis, which can estimate local maxima of the conditional p... [more] IBISML2017-57
pp.169-176
HCS 2016-08-19
15:10
Kyoto   Multimodal interaction in task-oriented collaboration -- A case study of "Marshmallow challenge" --
Noriko Suzuki (Tezukayama Univ.), Haruka Shoda (Ritsumeikan Univ.), Noriko Ito, Kaori Inada, Mamiko Sakata (Doshisha Univ.) HCS2016-35
We report the analyses of multiparty collaboration in the “Marshmallow challenge” task, by examining the relationship am... [more] HCS2016-35
pp.25-28
PRMU, HIP 2012-03-30
15:20
Hyogo   Discrimination of Unknown Objects from Known Objects Using Multimodal Information
Yuko Ozasa, Yasuo Ariki (Kobe Univ.), Naoto Iwahashi (NICT), Mikio Nakano (HRI-JP) PRMU2011-280 HIP2011-108
This paper presents a new method for detecting unknown objects and their unknown names in object
manipulation through m... [more]
PRMU2011-280 HIP2011-108
pp.247-252
SP 2011-06-23
16:00
Aichi Nagoya Univ. Model Adaptation using Audio-visual Interaction for Multi-modal Speech Recognition
Masanao Oonishi, Satoshi Tamura, Satoru Hayamizu (Gifu Univ.) SP2011-33
This paper investigates a linear-regressive model adaptation method, i.e. MLLR (Maximum Likelihood Linear Regression), f... [more] SP2011-33
pp.17-22
HIP 2009-12-18
12:05
Miyagi RIEC Selection and integration of pictorial and linguistic information in the determination of a sense of speed -- Motion-line and onomatopoeia --
Kayo Miura (Kyushu Univ.), Shunsuke Uemura (Nissha Printing Co.,Ltd.) HIP2009-113
Motion lines are effective cue to represent the direction, trajectory, appearance and a sense of speed of motion of obje... [more] HIP2009-113
pp.97-101
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