Survey: Transformer-based Models in Data Modality Conversion

Typically, a modality is linked to a particular sensor that creates a distinct communication channel, such as sight, speech, and written language. Humans possess a fundamental process in sensory perception that allows them to efficiently engage with the world in dynamic and unconstrained situations by integrating data from several sensory modalities. Each modality functions as a separate source of information that is distinguished by its own specific statistical features. A photograph depicting "elephants playing in the water" delivers visual information through numerous pixels, whereas a similar verbal description conveys this sight using distinct words. Similarly, voice can communicate the same occurrence using spectrograms or speech characteristics. A data conversion AI system must receive input from a specific modality, process, understand, and reproduce its content in a different modality, imitating human-like perception. Modality Conversion (MC) is a broad methodology for constructing artificial intelligence models that can extract and transform information from one modality of representation to another [67]. Amir Eskandari and Aman Anand contributed equally to this research.