Driven by the progress of large-scale pre-training, parameter-efficient transfer learning has
gained immense popularity across different subfields of Artificial Intelligence. The core is to
adapt the model to downstream tasks with only a small set of parameters. Recently,
researchers have leveraged such proven techniques in multimodal tasks and achieve
promising results. However, two critical issues remain unresolved: how to further reduce the
complexity with lightweight design and how to boost alignment between modalities under …