  Large Language Models (LLMs) have made the ambitious quest for generalist
agents significantly far from being a fantasy. A key hurdle for building such
general models is the diversity and heterogeneity of tasks and modalities. A
promising solution is unification, allowing the support of a myriad of tasks
and modalities within one unified framework. While few large models (e.g.,
Flamingo (Alayrac et al., 2022), trained on massive datasets, can support more
than two modalities, current small to mid-scale unified models are still
limited to 2 modalities, usually image-text or video-text. The question that we
ask is: is it possible to build efficiently a unified model that can support
all modalities? To answer this, we propose UnIVAL, a step further towards this
ambitious goal. Without relying on fancy datasets sizes or models with billions
of parameters, the ~ 0.25B parameter UnIVAL model goes beyond two modalities
and unifies text, images, video, and audio into a single model. Our model is
efficiently pretrained on many tasks, based on task balancing and multimodal
curriculum learning. UnIVAL shows competitive performance to existing
state-of-the-art approaches, across image and video-text tasks. The feature
representations learned from image and video-text modalities, allows the model
to achieve competitive performance when finetuned on audio-text tasks, despite
not being pretrained on audio. Thanks to the unified model, we propose a novel
study on multimodal model merging via weight interpolation of models trained on
different multimodal tasks, showing their benefits in particular for
out-of-distribution generalization. Finally, we motivate unification by showing
the synergy between tasks. The model weights and code are released here:
https://github.com/mshukor/UnIVAL.
