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- W4289243726 abstract "We introduce a new inference task - Visual Entailment (VE) - which differs from traditional Textual Entailment (TE) tasks whereby a premise is defined by an image, rather than a natural language sentence as in TE tasks. A novel dataset SNLI-VE (publicly available at https://github.com/necla-ml/SNLI-VE) is proposed for VE tasks based on the Stanford Natural Language Inference corpus and Flickr30k. We introduce a differentiable architecture called the Explainable Visual Entailment model (EVE) to tackle the VE problem. EVE and several other state-of-the-art visual question answering (VQA) based models are evaluated on the SNLI-VE dataset, facilitating grounded language understanding and providing insights on how modern VQA based models perform." @default.
- W4289243726 created "2022-08-01" @default.
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- W4289243726 date "2018-11-26" @default.
- W4289243726 modified "2023-09-24" @default.
- W4289243726 title "Visual Entailment Task for Visually-Grounded Language Learning" @default.
- W4289243726 doi "https://doi.org/10.48550/arxiv.1811.10582" @default.
- W4289243726 hasPublicationYear "2018" @default.
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