A Study on Deep Learning Performances of Identifying Images’ Emotion: Comparing Performances of Three Algorithms to Analyze Fashion Items
Emotion recognition using AI has garnered significant attention in recent years, particularly in areas such as fashion, where understanding consumer sentiment can drive more personalized and effective marketing strategies. This study aims to propose an AI model that automatically analyzes the emotio...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/3318 |
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| author | Gaeun Lee Seoyun Yi Jongtae Lee |
| author_facet | Gaeun Lee Seoyun Yi Jongtae Lee |
| author_sort | Gaeun Lee |
| collection | DOAJ |
| description | Emotion recognition using AI has garnered significant attention in recent years, particularly in areas such as fashion, where understanding consumer sentiment can drive more personalized and effective marketing strategies. This study aims to propose an AI model that automatically analyzes the emotional emotions of fashion images and compares the performance of CNN, ViT, and ResNet to determine the most suitable model. The experimental results showed that the vision transformer (ViT) model outperformed both ResNet50 and CNN models. This is due to the fact that transformer-based models, like ViT, offer greater scalability compared to CNN-based models. Specifically, ViT utilizes the transformer structure directly, which requires fewer computational resources during transfer learning compared to CNNs. This study illustrates that vision transformer (ViT) demonstrates higher performances with fewer computational resources than CNN during transfer learning. For academic and practical implications, the strong performance of ViT demonstrates the scalability and efficiency of transformer structures, indicating the need for further research applying transformer-based models to diverse datasets and environments. |
| format | Article |
| id | doaj-art-537473ef572d45708dee07628f5a96f6 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-537473ef572d45708dee07628f5a96f62025-08-20T02:42:41ZengMDPI AGApplied Sciences2076-34172025-03-01156331810.3390/app15063318A Study on Deep Learning Performances of Identifying Images’ Emotion: Comparing Performances of Three Algorithms to Analyze Fashion ItemsGaeun Lee0Seoyun Yi1Jongtae Lee2Department of Business Administration, Seoul Women’s University, Seoul 03079, Republic of KoreaDepartment of Data Science, Seoul Women’s University, Seoul 03079, Republic of KoreaDepartment of Business Administration, Seoul Women’s University, Seoul 03079, Republic of KoreaEmotion recognition using AI has garnered significant attention in recent years, particularly in areas such as fashion, where understanding consumer sentiment can drive more personalized and effective marketing strategies. This study aims to propose an AI model that automatically analyzes the emotional emotions of fashion images and compares the performance of CNN, ViT, and ResNet to determine the most suitable model. The experimental results showed that the vision transformer (ViT) model outperformed both ResNet50 and CNN models. This is due to the fact that transformer-based models, like ViT, offer greater scalability compared to CNN-based models. Specifically, ViT utilizes the transformer structure directly, which requires fewer computational resources during transfer learning compared to CNNs. This study illustrates that vision transformer (ViT) demonstrates higher performances with fewer computational resources than CNN during transfer learning. For academic and practical implications, the strong performance of ViT demonstrates the scalability and efficiency of transformer structures, indicating the need for further research applying transformer-based models to diverse datasets and environments.https://www.mdpi.com/2076-3417/15/6/3318vision transformerCNNResNetemotion forecastartificial intelligence |
| spellingShingle | Gaeun Lee Seoyun Yi Jongtae Lee A Study on Deep Learning Performances of Identifying Images’ Emotion: Comparing Performances of Three Algorithms to Analyze Fashion Items Applied Sciences vision transformer CNN ResNet emotion forecast artificial intelligence |
| title | A Study on Deep Learning Performances of Identifying Images’ Emotion: Comparing Performances of Three Algorithms to Analyze Fashion Items |
| title_full | A Study on Deep Learning Performances of Identifying Images’ Emotion: Comparing Performances of Three Algorithms to Analyze Fashion Items |
| title_fullStr | A Study on Deep Learning Performances of Identifying Images’ Emotion: Comparing Performances of Three Algorithms to Analyze Fashion Items |
| title_full_unstemmed | A Study on Deep Learning Performances of Identifying Images’ Emotion: Comparing Performances of Three Algorithms to Analyze Fashion Items |
| title_short | A Study on Deep Learning Performances of Identifying Images’ Emotion: Comparing Performances of Three Algorithms to Analyze Fashion Items |
| title_sort | study on deep learning performances of identifying images emotion comparing performances of three algorithms to analyze fashion items |
| topic | vision transformer CNN ResNet emotion forecast artificial intelligence |
| url | https://www.mdpi.com/2076-3417/15/6/3318 |
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