Summary of Tmt: Tri-modal Translation Between Speech, Image, and Text by Processing Different Modalities As Different Languages, By Minsu Kim et al.
TMT: Tri-Modal Translation between Speech, Image, and Text by Processing Different Modalities as Different Languages
by Minsu Kim, Jee-weon Jung, Hyeongseop Rha, Soumi Maiti, Siddhant Arora, Xuankai Chang, Shinji Watanabe, Yong Man Ro
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel Tri-Modal Translation (TMT) model proposes a unified approach to processing multi-modal information from speech, image, and text modalities. By tokenizing data into discrete tokens, the TMT reduces computational costs while maintaining translation quality. The model interprets different modalities as languages, enabling machine translation principles to be applied. Experimental results on six modality translation tasks show that TMT outperforms single-model counterparts, highlighting the benefits of unifying tasks for both practicality and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to work with different types of information is being developed. Imagine if you could easily move from one type of data (like speech or images) to another (like text). This is what a team has created in their Tri-Modal Translation (TMT) model. They think about different types of data as different languages, and then use the same ideas that help translate words between languages to translate information between these different types. |
Keywords
» Artificial intelligence » Multi modal » Translation
