  We initiate the first empirical study on the use of MLP architectures for
vision-and-language (VL) fusion. Through extensive experiments on 5 VL tasks
and 5 robust VQA benchmarks, we find that: (i) Without pre-training, using MLPs
for multimodal fusion has a noticeable performance gap compared to
transformers; (ii) However, VL pre-training can help close the performance gap;
(iii) Instead of heavy multi-head attention, adding tiny one-head attention to
MLPs is sufficient to achieve comparable performance to transformers. Moreover,
we also find that the performance gap between MLPs and transformers is not
widened when being evaluated on the harder robust VQA benchmarks, suggesting
using MLPs for VL fusion can generalize roughly to a similar degree as using
transformers. These results hint that MLPs can effectively learn to align
vision and text features extracted from lower-level encoders without heavy
reliance on self-attention. Based on this, we ask an even bolder question: can
we have an all-MLP architecture for VL modeling, where both VL fusion and the
vision encoder are replaced with MLPs? Our result shows that an all-MLP VL
model is sub-optimal compared to state-of-the-art full-featured VL models when
both of them get pre-trained. However, pre-training an all-MLP can surprisingly
achieve a better average score than full-featured transformer models without
pre-training. This indicates the potential of large-scale pre-training of
MLP-like architectures for VL modeling and inspires the future research
direction on simplifying well-established VL modeling with less inductive
design bias. Our code is publicly available at:
https://github.com/easonnie/mlp-vil
