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While sophisticated Visual Question Answering models have achieved remarkablesuccess, they tend to answer questions only according to superficialcorrelations between question and answer. Several recent approaches have beendeveloped to address this language priors problem. However, most of thempredict the correct answer according to one best output without checking theauthenticity of answers. Besides, they only explore the interaction betweenimage and question, ignoring the semantics of candidate answers. In this paper,we propose a select-and-rerank (SAR) progressive framework based on VisualEntailment. Specifically, we first select the candidate answers relevant to thequestion or the image, then we rerank the candidate answers by a visualentailment task, which verifies whether the image semantically entails thesynthetic statement of the question and each candidate answer. Experimentalresults show the effectiveness of our proposed framework, which establishes anew state-of-the-art accuracy on VQA-CP v2 with a 7.55% improvement.

Check It Again: Progressive Visual Question Answering via Visual Entailment