Visual Madlibs: Fill in the Blank Description Generation and Question Answering

Licheng Yu, Eunbyung Park, Alexander C. Berg, Tamara L. Berg; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2461-2469

In this paper, we introduce a new dataset consisting of 360,001 focused natural language descriptions for 10,738 images. This dataset, the Visual Madlibs dataset, is collected using automatically produced fill-in-the-blank templates designed to gather targeted descriptions about: people and objects, their appearances, activities, and interactions, as well as inferences about the general scene or its broader context. We provide several analyses of the Visual Madlibs dataset and demonstrate its applicability to two new description generation tasks: focused description generation, and multiple-choice question-answering for images. Experiments using joint-embedding and deep learning methods show promising results on these tasks.