FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering
University of Cambridge（剑桥大学）；University of Washington（华盛顿大学）
英文摘要：
The widely used Fact-based Visual Question Answering (FVQA) dataset contains visually-grounded questions that require information retrieval using common sense knowledge graphs to answer. It has been observed that the original dataset is highly imbalanced and concentrated on a small portion of its associated knowledge graph. We introduce FVQA 2.0 which contains adversarial variants of test questions to address this imbalance. We show that systems trained with the original FVQA train sets can be vulnerable to adversarial samples and we demonstrate an augmentation scheme to reduce this vulnerability without human annotations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
