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In order to achieve a general visual question answering (VQA) system, it isessential to learn to answer deeper questions that require compositionalreasoning on the image and external knowledge. Meanwhile, the reasoning processshould be explicit and explainable to understand the working mechanism of themodel. It is effortless for human but challenging for machines. In this paper,we propose a Hierarchical Graph Neural Module Network (HGNMN) that reasons overmulti-layer graphs with neural modules to address the above issues.Specifically, we first encode the image by multi-layer graphs from the visual,semantic and commonsense views since the clues that support the answer mayexist in different modalities. Our model consists of several well-designedneural modules that perform specific functions over graphs, which can be usedto conduct multi-step reasoning within and between different graphs. Comparedto existing modular networks, we extend visual reasoning from one graph to moregraphs. We can explicitly trace the reasoning process according to moduleweights and graph attentions. Experiments show that our model not only achievesstate-of-the-art performance on the CRIC dataset but also obtains explicit andexplainable reasoning procedures.

From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answering