Chain of Reasoning for Visual Question Answering
Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)
Chenfei Wu, Jinlai Liu, Xiaojie Wang, Xuan Dong
Reasoning plays an essential role in Visual Question Answering (VQA). Multi-step and dynamic reasoning is often necessary for answering complex questions. For example, a question "What is placed next to the bus on the right of the picture?" talks about a compound object "bus on the right," which is generated by the relation . Furthermore, a new relation including this compound object is then required to infer the answer. However, previous methods support either one-step or static reasoning, without updating relations or generating compound objects. This paper proposes a novel reasoning model for addressing these problems. A chain of reasoning (CoR) is constructed for supporting multi-step and dynamic reasoning on changed relations and objects. In detail, iteratively, the relational reasoning operations form new relations between objects, and the object refining operations generate new compound objects from relations. We achieve new state-of-the-art results on four publicly available datasets. The visualization of the chain of reasoning illustrates the progress that the CoR generates new compound objects that lead to the answer of the question step by step.