  People perceive the world with multiple senses (e.g., through hearing sounds,
reading words and seeing objects). However, most existing AI systems only
process an individual modality. This paper presents an approach that excels at
handling multiple modalities of information with a single model. In our
"{SkillNet}" model, different parts of the parameters are specialized for
processing different modalities. Unlike traditional dense models that always
activate all the model parameters, our model sparsely activates parts of the
parameters whose skills are relevant to the task. Such model design enables
SkillNet to learn skills in a more interpretable way. We develop our model for
five modalities including text, image, sound, video and code. Results show
that, SkillNet performs comparably to five modality-specific fine-tuned models.
Moreover, our model supports self-supervised pretraining with the same sparsely
activated way, resulting in better initialized parameters for different
modalities. We find that pretraining significantly improves the performance of
SkillNet on five modalities, on par with or even better than baselines with
modality-specific pretraining. On the task of Chinese text-to-image retrieval,
our final system achieves higher accuracy than existing leading systems
including Wukong{ViT-B} and Wenlan 2.0 while using less number of activated
parameters.
