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In this paper, we propose QACE, a new metric based on Question Answering forCaption Evaluation. QACE generates questions on the evaluated caption andchecks its content by asking the questions on either the reference caption orthe source image. We first develop QACE-Ref that compares the answers of theevaluated caption to its reference, and report competitive results with thestate-of-the-art metrics. To go further, we propose QACE-Img, which asks thequestions directly on the image, instead of reference. A Visual-QA system isnecessary for QACE-Img. Unfortunately, the standard VQA models are framed as aclassification among only a few thousand categories. Instead, we proposeVisual-T5, an abstractive VQA system. The resulting metric, QACE-Img ismulti-modal, reference-less, and explainable. Our experiments show thatQACE-Img compares favorably w.r.t. other reference-less metrics. We willrelease the pre-trained models to compute QACE.

QACE: Asking Questions to Evaluate an Image Caption