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Existing visual reasoning datasets such as Visual Question Answering (VQA),often suffer from biases conditioned on the question, image or answerdistributions. The recently proposed CLEVR dataset addresses these limitationsand requires fine-grained reasoning but the dataset is synthetic and consistsof similar objects and sentence structures across the dataset.  In this paper, we introduce a new inference task, Visual Entailment (VE) -consisting of image-sentence pairs whereby a premise is defined by an image,rather than a natural language sentence as in traditional Textual Entailmenttasks. The goal of a trained VE model is to predict whether the imagesemantically entails the text. To realize this task, we build a dataset SNLI-VEbased on the Stanford Natural Language Inference corpus and Flickr30k dataset.We evaluate various existing VQA baselines and build a model called ExplainableVisual Entailment (EVE) system to address the VE task. EVE achieves up to 71%accuracy and outperforms several other state-of-the-art VQA based models.Finally, we demonstrate the explainability of EVE through cross-modal attentionvisualizations. The SNLI-VE dataset is publicly available athttps://github.com/ necla-ml/SNLI-VE.

Visual Entailment: A Novel Task for Fine-Grained Image Understanding