Visual Question Answering (VQA) is the task of taking as input an image and a free-form natural language question about the image, and producing an accurate answer. In this work we view VQA as a "feature extraction" module to extract image and caption representations. We employ these representations for the task of image-caption ranking. Each feature dimension captures (imagines) whether a fact (question-answer pair) could plausibly be true for the image and caption. This allows the model to interpret images and captions from a wide variety of perspectives. We propose score-level and representation-level fusion models to incorporate VQA knowledge in an existing state-of-the-art VQA-agnostic image-caption ranking model. We find that incorporating and reasoning about consistency between images and captions significantly improves performance. Concretely, our model improves state-of-the-art on caption retrieval by 7.1% and on image retrieval by 4.4% on the MSCOCO dataset.

本研究将视觉问题回答任务视为“特征提取”模块，提取图像和标题的表征，以此为基础对图像-标题进行排序并提出融合模型提高图像-标题匹配一致性的表现。实验发现，该模型在MSCOCO数据集上的字幕检索提高了7.1％，图像提取提高了4.4％。

利用视觉问答进行图像字幕排名