Foundation Models for Speech, Images, Videos, and Control
Abstract
Abstract Foundation Models are able to model not only tokens of natural language but also token elements of arbitrary sequences. For images, square image patches can be represented as tokens; for videos, we can define tubelets that span an image patch across multiple frames. Subsequently, the proven self-attention algorithms can be applied to these tokens. Most importantly, several modalities like text and images can be processed in the same sequence allowing, for instance, the generation of images from text and text descriptions from video. In addition, the models are scalable to very large networks and huge datasets. The following multimedia types are covered in the subsequent sections. Speech recognition and text-to-speech models describe the translation of spoken language into text and vice versa. Image processing has the task to interpret images, describe them by captions, and generate new images according to textual descriptions. Video interpretation aims at recognizing action in videos and describing them through text. Furthermore, new videos can be created according to a textual description. Dynamical system trajectories characterize sequential decision problems, which can be simulated and controlled. DNA and protein sequences can be analyzed with Foundation Models to predict the structure and properties of the corresponding molecules.
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