论文标题
通过比较竞争解释来改善VQA及其解释\\
Improving VQA and its Explanations \\ by Comparing Competing Explanations
论文作者
论文摘要
最新的最新视觉问题回答（VQA）系统是不透明的黑匣子，只有在问题和视觉内容的情况下训练以适合答案分布。结果，这些系统经常采用快捷方式，专注于简单的视觉概念或问题先验。随着问题变得复杂，需要更多的推理和常识性知识，这种现象变得更加问题。为了解决这个问题，我们提出了一个新颖的框架，该框架使用解释来帮助VQA系统选择正确的答案。通过对人类文本解释的培训，我们的框架为问题和视觉内容构建了更好的表示形式，然后使用培训集中的生成或检索解释的答案候选者中的回答候选人中的信心。我们在VQA-X数据集上评估了我们的框架，该数据集在人类的解释中遇到了更困难的问题，从而在VQA及其解释上都取得了新的最新结果。
Most recent state-of-the-art Visual Question Answering (VQA) systems are opaque black boxes that are only trained to fit the answer distribution given the question and visual content. As a result, these systems frequently take shortcuts, focusing on simple visual concepts or question priors. This phenomenon becomes more problematic as the questions become complex that requires more reasoning and commonsense knowledge. To address this issue, we present a novel framework that uses explanations for competing answers to help VQA systems select the correct answer. By training on human textual explanations, our framework builds better representations for the questions and visual content, and then reweights confidences in the answer candidates using either generated or retrieved explanations from the training set. We evaluate our framework on the VQA-X dataset, which has more difficult questions with human explanations, achieving new state-of-the-art results on both VQA and its explanations.
