  The Visual Question Answering (VQA) task utilizes both visual image and
language analysis to answer a textual question with respect to an image. It has
been a popular research topic with an increasing number of real-world
applications in the last decade. This paper describes our recent research of
AliceMind-MMU (ALIbaba's Collection of Encoder-decoders from Machine
IntelligeNce lab of Damo academy - MultiMedia Understanding) that obtains
similar or even slightly better results than human being does on VQA. This is
achieved by systematically improving the VQA pipeline including: (1)
pre-training with comprehensive visual and textual feature representation; (2)
effective cross-modal interaction with learning to attend; and (3) A novel
knowledge mining framework with specialized expert modules for the complex VQA
task. Treating different types of visual questions with corresponding expertise
needed plays an important role in boosting the performance of our VQA
architecture up to the human level. An extensive set of experiments and
analysis are conducted to demonstrate the effectiveness of the new research
work.
