  Recent advances in vision-language pre-training (VLP) have demonstrated
impressive performance in a range of vision-language (VL) tasks. However, there
exist several challenges for measuring the community's progress in building
general multi-modal intelligence. First, most of the downstream VL datasets are
annotated using raw images that are already seen during pre-training, which may
result in an overestimation of current VLP models' generalization ability.
Second, recent VLP work mainly focuses on absolute performance but overlooks
the efficiency-performance trade-off, which is also an important indicator for
measuring progress.
  To this end, we introduce the Vision-Language Understanding Evaluation (VLUE)
benchmark, a multi-task multi-dimension benchmark for evaluating the
generalization capabilities and the efficiency-performance trade-off (``Pareto
SOTA'') of VLP models. We demonstrate that there is a sizable generalization
gap for all VLP models when testing on out-of-distribution test sets annotated
on images from a more diverse distribution that spreads across cultures.
Moreover, we find that measuring the efficiency-performance trade-off of VLP
models leads to complementary insights for several design choices of VLP. We
release the VLUE benchmark to promote research on building vision-language
models that generalize well to more diverse images and concepts unseen during
pre-training, and are practical in terms of efficiency-performance trade-off.
