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We introduce a new multi-modal task for computer systems, posed as a combinedvision-language comprehension challenge: identifying the most suitable textdescribing a scene, given several similar options. Accomplishing the taskentails demonstrating comprehension beyond just recognizing "keywords" (orkey-phrases) and their corresponding visual concepts. Instead, it requires analignment between the representations of the two modalities that achieves avisually-grounded "understanding" of various linguistic elements and theirdependencies. This new task also admits an easy-to-compute and well-studiedmetric: the accuracy in detecting the true target among the decoys.  The paper makes several contributions: an effective and extensible mechanismfor generating decoys from (human-created) image captions; an instance ofapplying this mechanism, yielding a large-scale machine comprehension dataset(based on the COCO images and captions) that we make publicly available; humanevaluation results on this dataset, informing a performance upper-bound; andseveral baseline and competitive learning approaches that illustrate theutility of the proposed task and dataset in advancing both image and languagecomprehension. We also show that, in a multi-task learning setting, theperformance on the proposed task is positively correlated with the end-to-endtask of image captioning.

Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task