The Visual Spatial Reasoning (VSR) corpus is a collection of caption-image pairs with true/false labels. Each caption describes the spatial relation of two individual objects in the image, and a vision-language model (VLM) needs to judge whether the caption is correctly describing the image (True) or not (False).
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e-SNLI-VE is a large VL (vision-language) dataset with NLEs (natural language explanations) with over 430k instances for which the explanations rely on the image content. It has been built by merging the explanations from e-SNLI and the image-sentence pairs from SNLI-VE.
15 PAPERS • 2 BENCHMARKS
Visual Entailment (VE) consists of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal of a trained VE model is to predict whether the image semantically entails the text. SNLI-VE is a dataset for VE which is based on the Stanford Natural Language Inference corpus and Flickr30k dataset.
110 PAPERS • 2 BENCHMARKS