  With ever increasing parameters and computation, vision-language pre-trained
(VLP) models exhibit prohibitive expenditure in downstream task adaption.
Recent endeavors mainly focus on parameter efficient transfer learning (PETL)
for VLP models by only updating a small number of parameters. However,
excessive computational overhead still plagues the application of VLPs. In this
paper, we aim at parameter and computation efficient transfer learning (PCETL)
for VLP models. In particular, PCETL not only needs to limit the number of
trainable parameters in VLP models, but also to reduce the computational
redundancy during inference, thus enabling a more efficient transfer. To
approach this target, we propose a novel dynamic architecture skipping (DAS)
approach towards effective PCETL. Instead of directly optimizing the intrinsic
architectures of VLP models, DAS first observes the significances of their
modules to downstream tasks via a reinforcement learning (RL) based process,
and then skips the redundant ones with lightweight networks, i.e., adapters,
according to the obtained rewards. In this case, the VLP model can well
maintain the scale of trainable parameters while speeding up its inference on
downstream tasks. To validate DAS, we apply it to two representative VLP
models, namely ViLT and METER, and conduct extensive experiments on a bunch of
VL tasks. The experimental results not only show the great advantages of DAS in
reducing computational complexity, e.g. -11.97% FLOPs of METER on VQA2.0, but
also confirm its competitiveness against existing PETL methods in terms of
parameter scale and performance. Our source code is given in our appendix.
