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Many vision and language tasks require commonsense reasoning beyonddata-driven image and natural language processing. Here we adopt VisualQuestion Answering (VQA) as an example task, where a system is expected toanswer a question in natural language about an image. Current state-of-the-artsystems attempted to solve the task using deep neural architectures andachieved promising performance. However, the resulting systems are generallyopaque and they struggle in understanding questions for which extra knowledgeis required. In this paper, we present an explicit reasoning layer on top of aset of penultimate neural network based systems. The reasoning layer enablesreasoning and answering questions where additional knowledge is required, andat the same time provides an interpretable interface to the end users.Specifically, the reasoning layer adopts a Probabilistic Soft Logic (PSL) basedengine to reason over a basket of inputs: visual relations, the semantic parseof the question, and background ontological knowledge from word2vec andConceptNet. Experimental analysis of the answers and the key evidentialpredicates generated on the VQA dataset validate our approach.

Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering