TMT: Tri-Modal Translation Between Speech, Image, and Text by Processing Different Modalities as Different Languages

The capability to jointly process multi-modal information is becoming essential. However, the development of multi-modal learning is hindered by the substantial computational requirements and the limited availability of paired multi-modal data. We propose a novel Tri-Modal Translation (TMT) model that translates between arbitrary modalities spanning speech, image, and text. We introduce a simple yet efficient and effective approach, treating speech and image modalities as discrete text modality and approaching multi-modal translation as a well-established machine translation problem. To this end, we tokenize speech and image data into discrete tokens, resulting in a significant reduction in computational cost. Furthermore, by incorporating back translation into multi-modal translation, unpaired data can also be utilized for training. TMT can perform six modality translation tasks and consistently outperforms its single-model counterparts. TMT significantly reduces the required data size (in bits) for training, to approximately 0.2% for speech data and 0.04% for image data, respectively.