scholarly journals A deep learning‐based image captioning method to automatically generate comprehensive explanations of bridge damage

2021 ◽  
Author(s):  
Pang‐Jo Chun ◽  
Tatsuro Yamane ◽  
Yu Maemura
Keyword(s):  
Deep Learning ◽  
Image Captioning ◽  
Bridge Damage

A Deep Learning Approach For Bangla Image Captioning System

2021 ◽  
Author(s):  
Toshiba Kamruzzaman ◽  
Soomanib Kamruzzaman ◽  
Abir Zaman
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Image Captioning

scholarly journals Bornon: Bengali Image Captioning with Transformer-Based Deep Learning Approach

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Faisal Muhammad Shah ◽  
Mayeesha Humaira ◽  
Md Abidur Rahman Khan Jim ◽  
Amit Saha Ami ◽  
Shimul Paul
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Image Captioning

scholarly journals Korean Tourist Spot Multi-Modal Dataset for Deep Learning Applications

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 139
Author(s):  
Changhoon Jeong ◽  
Sung-Eun Jang ◽  
Sanghyuck Na ◽  
Juntae Kim
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Image Captioning ◽  
Learning Research

Recently, deep learning-based methods for solving multi-modal tasks such as image captioning, multi-modal classification, and cross-modal retrieval have attracted much attention. To apply deep learning for such tasks, large amounts of data are needed for training. However, although there are several Korean single-modal datasets, there are not enough Korean multi-modal datasets. In this paper, we introduce a KTS (Korean tourist spot) dataset for Korean multi-modal deep-learning research. The KTS dataset has four modalities (image, text, hashtags, and likes) and consists of 10 classes related to Korean tourist spots. All data were extracted from Instagram and preprocessed. We performed two experiments, image classification and image captioning with the dataset, and they showed appropriate results. We hope that many researchers will use this dataset for multi-modal deep-learning research.

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