  There is a key problem in the medical visual question answering task that how
to effectively realize the feature fusion of language and medical images with
limited datasets. In order to better utilize multi-scale information of medical
images, previous methods directly embed the multi-stage visual feature maps as
tokens of same size respectively and fuse them with text representation.
However, this will cause the confusion of visual features at different stages.
To this end, we propose a simple but powerful multi-stage feature fusion
method, MF2-MVQA, which stage-wise fuses multi-level visual features with
textual semantics. MF2-MVQA achieves the State-Of-The-Art performance on
VQA-Med 2019 and VQA-RAD dataset. The results of visualization also verify that
our model outperforms previous work.
