{"url":"/dataset/vqa-e","name":"VQA-E","full_name":null,"description_markdown":"VQA-E is a dataset for Visual Question Answering with Explanation, where the models are required to generate and explanation with the predicted answer. The VQA-E dataset is automatically derived from the VQA v2 dataset by synthesizing a textual explanation for each image-question-answer triple.\r\n\r\nImage Source: [VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions](https://arxiv.org/abs/1803.07464)","description_withheld":null,"homepage":"https://github.com/liqing-ustc/VQA-E","introduced_date":"2018-03-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/vqa-e-explaining-elaborating-and-enhancing","title":"VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions","first_author":"Qing Li","url":null},"license":{"name":"MIT","url":"https://github.com/liqing-ustc/VQA-E"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["VQA-E"],"data_loaders":[{"repo":"https://github.com/liqing-ustc/VQA-E","url":"https://github.com/liqing-ustc/VQA-E","frameworks":[]}],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}