  Large-scale Transformer models bring significant improvements for various
downstream vision language tasks with a unified architecture. The performance
improvements come with increasing model size, resulting in slow inference speed
and increased cost for severing. While some certain predictions benefit from
the full complexity of the large-scale model, not all of inputs need the same
amount of computation to conduct, potentially leading to computation resource
waste. To handle this challenge, early exiting is proposed to adaptively
allocate computational power in term of input complexity to improve inference
efficiency. The existing early exiting strategies usually adopt output
confidence based on intermediate layers as a proxy of input complexity to incur
the decision of skipping following layers. However, such strategies cannot
apply to encoder in the widely-used unified architecture with both encoder and
decoder due to difficulty of output confidence estimation in the encoder. It is
suboptimal in term of saving computation power to ignore the early exiting in
encoder component. To handle this challenge, we propose a novel early exiting
strategy for unified visual language models, which allows dynamically skip the
layers in encoder and decoder simultaneously in term of input layer-wise
similarities with multiple times of early exiting, namely \textbf{MuE}. By
decomposing the image and text modalities in the encoder, MuE is flexible and
can skip different layers in term of modalities, advancing the inference
efficiency while minimizing performance drop. Experiments on the SNLI-VE and MS
COCO datasets show that the proposed approach MuE can reduce expected inference
time by up to 50\% and 40\% while maintaining 99\% and 96\% performance
respectively.
