| Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
| ME, IEICE-EMM, IEICE-IE, IEICE-LOIS, IEE-CMN, IPSJ-AVM [detail] |
2023-09-07
09:50 |
Osaka |
Osaka Metropolitan Univ.
(Primary: On-site, Secondary: Online) |
Improving Performance of Convolutional Neural Network-Based Driver Behavior Recognition
Shengbiao Wang, Koji Iwano (Tokyo City Univ.) |
This study investigates the automatic recognition of driver behaviors using images captured by in-vehicle cameras for th... [more] |
ME2023-90
pp.7-12 |
| IIEEJ, AIT |
2022-10-30
16:20 |
Online |
on line |
Local Feature Alignment with Attention Mechanism for Cow Re-Identification
Feifan Zhang, Yota Yamamoto, Yukinobu Taniguchi (TUS) |
The purpose of this research is to improve the accuracy of dairy cows’ individual identification by image recognition us... [more] |
AIT2022-178
pp.23-26 |
| HI, IEICE-HIP, ASJ-H, VRPSY [detail] |
2022-02-27
13:00 |
Online |
on line |
An analysis of microsaccades and attentional direction induced by bottom-up attention shifts
Masahito Sakaguchi, Ryoma Kobata, Hisashi Yoshida, Takeshi Kohama (Kindai Univ.) |
This study experimented with inducing bottom-up attention shift to peripheral spotlight stimuli presented at random timi... [more] |
HI2022-1
pp.17-22 |
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-22
16:45 |
Online |
online |
A Note on Accurate Distress Classification Using Deep Learning Considering Confidence in Attention map
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a note on accurate distress classification using deep learning considering confidence in attention m... [more] |
MMS2022-33 ME2022-58 AIT2022-33
pp.371-376 |
| AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-22
17:00 |
Online |
online |
A Note on Distress Detection based on Deep Learning with Hierarchical Multi-Scale Attention Mechanism for Supporting Maintenance of Subway Tunnels
Saya Takada, Keisuke Maeda, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
In maintenance of transportation infrastructures, advanced support technologies that can reduce the burden on engineers ... [more] |
MMS2022-34 ME2022-59 AIT2022-34
pp.377-381 |
| ME |
2021-12-13
14:00 |
Online |
online |
Emotion Recognition from Gait Using Attention Spatial-Temporal Graph Convolutional Network
Shoji Kisita, Chen Yen-Wei (Ritsumeikan Univ.), Tomoko Tateyama (Shiga Univ.), Yutaro Iwamoto, Liu Jiaqing, Chai Shurong (Ritsumeikan Univ.) |
3D pose recognition is a technology that has been used in many fields, including touchless operation in the medical fiel... [more] |
ME2021-94
pp.25-28 |
| HI |
2021-03-05
14:05 |
Online |
|
[Short Paper]
Fault determination of electric motor coil by YOLO-v3 with Attention
Mizuki Kato (Ritsumeikan Univ Grad), Yutaro Iwamoto (Ritsumeikan Univ), Toshitaka Sugimoto, Toru Aiba (Kusatsu Electric), Yen-Wei Chen (Ritsumeikan Univ) |
Object detection has been widely applied to the visual inspection of factory products. However, since the detection mode... [more] |
HI2021-7
pp.25-26 |
| IEICE-IE, IEICE-ITS, MMS, ME, AIT [detail] |
2021-02-18
11:35 |
Online |
Online |
A Note on Accurate Distress Image Classification of Road Structures Using Attention Map based on Text Data
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a correlation-aware attention branch network (CorABN) using multi-modal data for deterioration level... [more] |
MMS2021-4 ME2021-4 AIT2021-4
pp.17-21 |
| HI, 3DMT |
2020-03-11
12:30 |
Tokyo |
Kogakuin Univ. Tokyo Urban Tech Tower Campus
(Cancelled) |
Higher-order textures that attract attention
-- Pop-out of material surfaces --
Kento Yoneoka (Tsukuba Univ.), Nobuhiko Wagatsuma (Toho Univ.), Ko Sakai (Tsukuba Univ.) |
Pop-out among textures depends on low-lever image features and has been known as an outcome of the neural mechanisms und... [more] |
HI2020-50 3DIT2020-1
pp.1-2 |
| HI, 3DMT |
2016-03-08
14:00 |
Tokyo |
Tokyo University of Agriculture and Technology |
A gaze prediction model for scan-path simulation of free-viewing
Hiroki Yoshino, Takeshi Kohama (Kinki Univ.) |
In the processes of gaze shifts on a visual object, cooperative behaviors of bottom-up attention and top–down atte... [more] |
HI2016-46 3DIT2016-5
pp.17-20 |
| HI, 3DMT |
2013-03-06
10:30 |
Tokyo |
Tokyo University of Agriculture and Technology |
A mathematical model of working memory system in higher order visual processing
Naoki Nishida, Takeshi Kohama (Kinki Univ.) |
The purpose of this study is to understand the function of working memories while seeking a target during visual search ... [more] |
HI2013-48 3DIT2013-16
pp.27-30 |
| HI, 3DMT |
2013-03-06
11:20 |
Tokyo |
Tokyo University of Agriculture and Technology |
A visual search model based on the spatio-temporal properties of visual attention in different feature domains.
Katsuya Yano, Takeshi Kohama (Kinki Univ.) |
Previous saliency-based visual attention models dealt with the mechanism underlying attentional shifts in the early visu... [more] |
HI2013-50 3DIT2013-18
pp.35-38 |
| HI, 3DMT |
2013-03-06
15:00 |
Tokyo |
Tokyo University of Agriculture and Technology |
Continuous inhibition of microsaccades in attentional concentration
Sho Endo, Takeshi Kohama, Daisuke Noguchi (Kinki Univ) |
Recent studies show that the mechanism responsible for the occurrence
of microsaccades, which are small involuntary shi... [more] |
HI2013-54 3DIT2013-22
pp.51-54 |
| HI |
2011-03-15
16:30 |
Tokyo |
Tokyo Univ. of Tech. |
Saliency map model for visual attention in depth
Taiki Gochi, Takeshi Kohama (Kinki Univ.) |
Previous saliency-based visual attention models dealt with the mechanism underlying attentional shifts in the early visu... [more] |
HI2011-37
pp.31-34 |