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Many real-world problems are inherently multimodal, from spoken language,gestures, and paralinguistics humans use to communicate, to force,proprioception, and visual sensors on robots. While there has been an explosionof interest in multimodal learning, these methods are focused on a small set ofmodalities primarily in language, vision, and audio. In order to accelerategeneralization towards diverse and understudied modalities, this paper studiesefficient representation learning for high-modality scenarios involving a largeset of diverse modalities. Since adding new models for every new modalitybecomes prohibitively expensive, a critical technical challenge isheterogeneity quantification: how can we measure which modalities encodesimilar information and interactions in order to permit parameter sharing withprevious modalities? This paper proposes two new information theoretic metricsfor heterogeneity quantification: (1) modality heterogeneity studies howsimilar 2 modalities {X1,X2} are by measuring how much information can betransferred from X1 to X2, while (2) interaction heterogeneity studies howsimilarly pairs of modalities {X1,X2}, {X3,X4} interact by measuring how muchinformation can be transferred from fusing {X1,X2} to {X3,X4}. We show theimportance of these 2 proposed metrics as a way to automatically prioritize thefusion of modalities that contain unique information or interactions. Theresult is a single model, HighMMT, that scales up to 10 modalities (text,image, audio, video, sensors, proprioception, speech, time-series, sets, andtables) and 15 tasks from 5 research areas. Not only does HighMMT outperformprior methods on the tradeoff between performance and efficiency, it alsodemonstrates a crucial scaling behavior: performance continues to improve witheach modality added, and it transfers to entirely new modalities and tasksduring fine-tuning.

High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning