Maintaining Reasoning Consistency in Compositional Visual Question Answering

Chenchen Jing, Yunde Jia, Yuwei Wu, Xinyu Liu, Qi Wu
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引用次数: 6

Abstract

A compositional question refers to a question that contains multiple visual concepts (e.g., objects, attributes, and relationships) and requires compositional reasoning to answer. Existing VQA models can answer a compositional question well, but cannot work well in terms of reasoning consistency in answering the compositional question and its sub-questions. For example, a compositional question for an image is: “Are there any elephants to the right of the white bird?” and one of its sub-questions is “Is any bird visible in the scene?”. The models may answer “yes” to the compositional question, but “no” to the sub-question. This paper presents a dialog-like reasoning method for maintaining reasoning consistency in answering a compositional question and its sub-questions. Our method integrates the reasoning processes for the sub-questions into the reasoning process for the compositional question like a dialog task, and uses a consistency constraint to penalize inconsistent answer predictions. In order to enable quantitative evaluation of reasoning consistency, we construct a GQA-Sub dataset based on the well-organized GQA dataset. Experimental results on the GQA dataset and the GQA-Sub dataset demonstrate the effectiveness of our method.
在作文式视觉问答中保持推理一致性
组合问题是指包含多个视觉概念(例如,对象、属性和关系)并需要组合推理来回答的问题。现有的VQA模型可以很好地回答组合问题,但在回答组合问题及其子问题的推理一致性方面表现不佳。例如,一张图片的构图问题是:“白鸟的右边有大象吗?”,其中一个子问题是“场景中有鸟吗?”模型可能对组合问题回答“是”,但对子问题回答“否”。本文提出了一种类似对话的推理方法,用于在回答组合题及其子题时保持推理一致性。我们的方法将子问题的推理过程集成到组合问题(如对话任务)的推理过程中,并使用一致性约束来惩罚不一致的答案预测。为了实现推理一致性的定量评估,我们在组织良好的GQA数据集的基础上构建了一个GQA- sub数据集。在GQA数据集和GQA- sub数据集上的实验结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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