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One of the main challenges of multimodal learning is the need to combineheterogeneous modalities (e.g., video, audio, text). For example, video andaudio are obtained at much higher rates than text and are roughly aligned intime. They are often not synchronized with text, which comes as a globalcontext, e.g., a title, or a description. Furthermore, video and audio inputsare of much larger volumes, and grow as the video length increases, whichnaturally requires more compute dedicated to these modalities and makesmodeling of long-range dependencies harder.  We here decouple the multimodal modeling, dividing it into separate, focusedautoregressive models, processing the inputs according to the characteristicsof the modalities. We propose a multimodal model, called Mirasol3B, consistingof an autoregressive component for the time-synchronized modalities (audio andvideo), and an autoregressive component for the context modalities which arenot necessarily aligned in time but are still sequential. To address thelong-sequences of the video-audio inputs, we propose to further partition thevideo and audio sequences in consecutive snippets and autoregressively processtheir representations. To that end, we propose a Combiner mechanism, whichmodels the audio-video information jointly within a timeframe. The Combinerlearns to extract audio and video features from raw spatio-temporal signals,and then learns to fuse these features producing compact but expressiverepresentations per snippet.  Our approach achieves the state-of-the-art on well established multimodalbenchmarks, outperforming much larger models. It effectively addresses the highcomputational demand of media inputs by both learning compact representations,controlling the sequence length of the audio-video feature representations, andmodeling their dependencies in time.

Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities