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Committee Date Time Place Paper Title / Authors Abstract Paper #
MVE 2021-09-17
Online Online Distracted Driving Classification with Multi-task Learning
Dichao Liu (NU), Toshihiko Yamasaki (Univ.Tokyo), Yu Wang (Rits), Kenji Mase (NU), Jien Kato (Rits)
PRMU 2020-12-18
Online Online Multi-Task Attention Learning for Fine-grained Recognition
Dichao Liu (NU), Yu Wang (Rits), Kenji Mase (NU), Jien Kato (Rits) PRMU2020-63
Due to its inter-class similarity and intra-class variation, Fine-Grained Image Classification (FGIC) is an intrinsicall... [more] PRMU2020-63
PRMU 2020-12-18
Online Online Rethinking the local similarity in content-based image retrieval
Longjiao Zhao (Nagoya Univ.), Yu Wang (Ritsumeikan Univ), Yoshiharu Ishikawa (Nagoya Univ.), Jien Kato (Ritsumeikan Univ) PRMU2020-68
Recently, Convolutional Neural Networks(CNN) have shown good performance in the image retrieval task. Especially, local ... [more] PRMU2020-68
PRMU, IPSJ-CVIM 2020-05-14
Online online Human Action Recognition with Two-stream 3D BagNet
Junya Uchida, Yu Wang, Jien Kato (Ritsumeikan Univ.) PRMU2020-3
We propose the Two-stream 3D BagNet for the human action recognition task. The proposed architecture is inspired by the ... [more] PRMU2020-3
PRMU, IPSJ-CVIM 2020-05-15
Online online Atmosphere Estimation in Conversation Scenes with Social Relations
Runa Sugiyama, Yu Wang, Jien Kato (Ritsumeikan Univ.) PRMU2020-5
The objective of this work is to estimate the atmosphere of the conversation scenes. Because the atmosphere is a subject... [more] PRMU2020-5
PRMU, IPSJ-CVIM 2020-03-16
(Cancelled but technical report was issued)
Continuous Variables Estimation Through Classification Networks Ensembles
Qianyuan Liu (Nagoya Univ.), Yu Wang, Jien Kato (Ritsumeikan Univ.) PRMU2019-87
With the development of deep learning, CNNs have shown outstanding performance on various tasks. Previous approaches fin... [more] PRMU2019-87
PRMU, IPSJ-CVIM 2020-03-17
(Cancelled but technical report was issued)
Pedestrian Detection using Wide and Narrow Area Networks
Misaki Kodaira (Nagoya Univ.), Yu Wang, Jien Kato (Ritsumeikan Univ.) PRMU2019-96
 [more] PRMU2019-96
PRMU, BioX 2018-03-18
Tokyo   Pedestrian Detection with Multi-level Deep Features
Misaki Kodaira, Yu Wang, Jien Kato (Nagoya Univ.) BioX2017-52 PRMU2017-188
In this research, we aim to clarify effective application of CNN features in pedestrian detection. In the experiment, fe... [more] BioX2017-52 PRMU2017-188
PRMU, BioX 2018-03-18
Tokyo   Image Retrieval with Augmented Fine-tuned CNN Features
Zhao Longjiao, Yu Wang, Jien Kato (Nagoya Univ.) BioX2017-55 PRMU2017-191
Recently, pre-trained CNN features have shown a good performance on the content-based image retrieval task. In this pape... [more] BioX2017-55 PRMU2017-191
PRMU 2017-12-17
Kanagawa   Hierarchical Multi-stream STNs for Fine-grained Action Recognition
Dichao Liu, Yu Wang, Jien Kato (Nagoya Univ.) PRMU2017-103
Fine-grained action recognition is a difficult problem mainly because that the discriminative information is usually ver... [more] PRMU2017-103
PRMU 2017-12-17
Kanagawa   BOJ253 -- A Large-scale Dataset for Butterfly Recognition --
Ruisheng Qin, Yu Wang, Jien Kato (Nagoya Univ.) PRMU2017-104
The purpose of this paper is to introduce a large-scale butterflies’ image dataset for butterfly recognition. The datase... [more] PRMU2017-104
PRMU 2017-12-17
Kanagawa   Estimation of cumbersome situation using in-vehicle camera image and Liders data
Hiroki Mitsui, Yu Wang, Jien Kato (Nagoya Univ.) PRMU2017-110
In recent years, researches have been actively conducted to assist driving and driving of cars, including detection of p... [more] PRMU2017-110
PRMU, CNR 2017-02-18
Hokkaido   Fine-grained Pedestrian Classification by Fusing Multiple CNN Models
Yoshihito Kokubo, Yu Wang, Jien Kato, Kenji Mase (Nagoya Univ.) PRMU2016-166 CNR2016-33
Fine-grained categories of pedestrians lead to higher level understanding on pedestrians as well as their activities, th... [more] PRMU2016-166 CNR2016-33
PRMU, CNR 2017-02-18
Hokkaido   Part-aware CNN for Pedestrian Detection
Cong Cao, Yu Wang, Jien Kato, Kenji Mase (Nagoya Univ.) PRMU2016-167 CNR2016-34
Pedestrian detection is a significant task in computer vision. In recent years, it is widely used in the applications su... [more] PRMU2016-167 CNR2016-34
PRMU, CNR 2017-02-18
Hokkaido   Evaluation of Triple-Stream Convolutional Network for Action Recognition
Dichao Liu, Yu Wang, Jien Kato, Kenji Mase (Nagoya Univ.) PRMU2016-168 CNR2016-35
Recently, Two-Stream Convolutional Network has achieved remarkable performance. For example, by using static frames and ... [more] PRMU2016-168 CNR2016-35
PRMU, CNR 2017-02-19
Hokkaido   More Convolution Filters for Better Image Retrieval
Longjiao Zhao, Yu Wang, Jien Kato, Kenji Mase (Nagoya Univ.) PRMU2016-194 CNR2016-61
Recently, a lot of works have shown the advantages of utilizing the deep descriptor on image retrieval,which uses the fe... [more] PRMU2016-194 CNR2016-61
PRMU, IE, MI, SIP 2016-05-20
Aichi   Pedestrian Detection using Mid-level Body Parts
Cong Cao, Guanwen Zhang, Yu Wang, Jien Kato, Kenji Mase (Nagoya Univ.) SIP2016-17 IE2016-17 PRMU2016-17 MI2016-17
 [more] SIP2016-17 IE2016-17 PRMU2016-17 MI2016-17
PRMU, IPSJ-CVIM, MVE [detail] 2016-01-22
Osaka   Fine-Grained Classification of Pedestrian using Statistical Information
Yoshihito Kokubo, Yu Wang, Jien Kato, Kenji Mase (NU) PRMU2015-124 MVE2015-46
The purpose of this study is fine-grained classification of sex(2 classes), age(5 classes), weight(3 classes), clothing(... [more] PRMU2015-124 MVE2015-46
PRMU, MI, IE, SIP 2015-05-14
Mie   Ground Plane Estimation from Monocular Camera using the Surface Layout
Yoshihito Kokubo, Yu Wang, Jien Kato, Kenji Mase (Nagoya Univ.) SIP2015-4 IE2015-4 PRMU2015-4 MI2015-4
Ground plane orientation is useful in many computer vision tasks. In this work, we propose a coarse-to-fine approach to ... [more] SIP2015-4 IE2015-4 PRMU2015-4 MI2015-4
PRMU, BioX 2015-03-20
Kanagawa   Video Level Violence Rating
Jien Kato, Yu Wang, Guanwen Zhang (Nagoya Unive.) BioX2014-78 PRMU2014-198
 [more] BioX2014-78 PRMU2014-198
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