  Human-like attention as a supervisory signal to guide neural attention has
shown significant promise but is currently limited to uni-modal integration -
even for inherently multimodal tasks such as visual question answering (VQA).
We present the Multimodal Human-like Attention Network (MULAN) - the first
method for multimodal integration of human-like attention on image and text
during training of VQA models. MULAN integrates attention predictions from two
state-of-the-art text and image saliency models into neural self-attention
layers of a recent transformer-based VQA model. Through evaluations on the
challenging VQAv2 dataset, we show that MULAN achieves a new state-of-the-art
performance of 73.98% accuracy on test-std and 73.72% on test-dev and, at the
same time, has approximately 80% fewer trainable parameters than prior work.
Overall, our work underlines the potential of integrating multimodal human-like
and neural attention for VQA
