Survey: Transformer-based Models in Data Modality Conversion
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929453845708800 |
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| author | Rashno, Elyas Eskandari, Amir Anand, Aman Zulkernine, Farhana |
| author_facet | Rashno, Elyas Eskandari, Amir Anand, Aman Zulkernine, Farhana |
| contents | Transformers have made significant strides across various artificial intelligence domains, including natural language processing, computer vision, and audio processing. This success has naturally garnered considerable interest from both academic and industry researchers. Consequently, numerous Transformer variants (often referred to as X-formers) have been developed for these fields. However, a thorough and systematic review of these modality-specific conversions remains lacking. Modality Conversion involves the transformation of data from one form of representation to another, mimicking the way humans integrate and interpret sensory information. This paper provides a comprehensive review of transformer-based models applied to the primary modalities of text, vision, and speech, discussing their architectures, conversion methodologies, and applications. By synthesizing the literature on modality conversion, this survey aims to underline the versatility and scalability of transformers in advancing AI-driven content generation and understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04723 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Survey: Transformer-based Models in Data Modality Conversion Rashno, Elyas Eskandari, Amir Anand, Aman Zulkernine, Farhana Image and Video Processing Artificial Intelligence Computation and Language Signal Processing Transformers have made significant strides across various artificial intelligence domains, including natural language processing, computer vision, and audio processing. This success has naturally garnered considerable interest from both academic and industry researchers. Consequently, numerous Transformer variants (often referred to as X-formers) have been developed for these fields. However, a thorough and systematic review of these modality-specific conversions remains lacking. Modality Conversion involves the transformation of data from one form of representation to another, mimicking the way humans integrate and interpret sensory information. This paper provides a comprehensive review of transformer-based models applied to the primary modalities of text, vision, and speech, discussing their architectures, conversion methodologies, and applications. By synthesizing the literature on modality conversion, this survey aims to underline the versatility and scalability of transformers in advancing AI-driven content generation and understanding. |
| title | Survey: Transformer-based Models in Data Modality Conversion |
| topic | Image and Video Processing Artificial Intelligence Computation and Language Signal Processing |
| url | https://arxiv.org/abs/2408.04723 |