Stephanie Zhou

Alane Suhr, Stephanie Zhou, Ally Zhang et al.

We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge.

Stephanie Zhou, Alane Suhr, Yoav Artzi

Natural language provides a widely accessible and expressive interface for robotic agents. To understand language in complex environments, agents must reason about the full range of language inputs and their correspondence to the world. Such reasoning over language and vision is an open problem that is receiving increasing attention. While existing data sets focus on visual diversity, they do not display the full range of natural language expressions, such as counting, set reasoning, and comparisons. We propose a simple task for natural language visual reasoning, where images are paired with descriptive statements. The task is to predict if a statement is true for the given scene. This abstract describes our existing synthetic images corpus and our current work on collecting real vision data.