ITE Technical Group Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2021-02-18 11:35
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.)
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents a correlation-aware attention branch network (CorABN) using multi-modal data for deterioration level estimation of infrastructures. CorABN can collaboratively use visual features from distress images and text features from text data recorded at the inspection to improve the estimation accuracy. Specifically, by maximizing the correlation between the visual and text features that provide useful information for the deterioration level estimation, a correlation-aware attention map can be generated. Besides, the text features are also utilized in the final estimation along with visual features improved by the attention mechanism. With the losses based on both the estimation accuracy and the correlation, CorABN can train the entire model in an end-to-end manner. Experiments using distress images and their corresponding text data show the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Distress image / deterioration level estimation / deep learning / correlation maximization / attention map / / /  
Reference Info. ITE Tech. Rep., vol. 45, no. 4, ME2021-4, pp. 17-21, Feb. 2021.
Paper # ME2021-4 
Date of Issue 2021-02-11 (MMS, ME, AIT) 
ISSN Print edition: ISSN 1342-6893  Online edition: ISSN 2424-1970
Download PDF

Conference Information
Committee IEICE-IE IEICE-ITS MMS ME AIT  
Conference Date 2021-02-18 - 2021-02-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To ME 
Conference Code 2021-02-IE-ITS-MMS-ME-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Note on Accurate Distress Image Classification of Road Structures Using Attention Map based on Text Data 
Sub Title (in English)  
Keyword(1) Distress image  
Keyword(2) deterioration level estimation  
Keyword(3) deep learning  
Keyword(4) correlation maximization  
Keyword(5) attention map  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Naoki Ogawa  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Keisuke Maeda  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Takahiro Ogawa  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Miki Haseyama  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker
Date Time 2021-02-18 11:35:00 
Presentation Time 25 
Registration for ME 
Paper # ITE-MMS2021-4,ITE-ME2021-4,ITE-AIT2021-4 
Volume (vol) ITE-45 
Number (no) no.4 
Page pp.17-21 
#Pages ITE-5 
Date of Issue ITE-MMS-2021-02-11,ITE-ME-2021-02-11,ITE-AIT-2021-02-11 


[Return to Top Page]

[Return to ITE Web Page]


The Institute of Image Information and Television Engineers (ITE), Japan