Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911755920211968 |
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| author | Che, Chang Lin, Qunwei Zhao, Xinyu Huang, Jiaxin Yu, Liqiang |
| author_facet | Che, Chang Lin, Qunwei Zhao, Xinyu Huang, Jiaxin Yu, Liqiang |
| contents | The process of transforming input images into corresponding textual explanations stands as a crucial and complex endeavor within the domains of computer vision and natural language processing. In this paper, we propose an innovative ensemble approach that harnesses the capabilities of Contrastive Language-Image Pretraining models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06167 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation Che, Chang Lin, Qunwei Zhao, Xinyu Huang, Jiaxin Yu, Liqiang Computer Vision and Pattern Recognition Artificial Intelligence The process of transforming input images into corresponding textual explanations stands as a crucial and complex endeavor within the domains of computer vision and natural language processing. In this paper, we propose an innovative ensemble approach that harnesses the capabilities of Contrastive Language-Image Pretraining models. |
| title | Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2401.06167 |