This study addresses generating counterfactual explanations with multimodal information. Our goal is not only to classify a video into a specific category, but also to provide explanations on why it is not predicted as part of a specific class with a combination of visual-linguistic information. Requirements that the expected output should satisfy are referred to as counterfactuality in this paper: (1) Compatibility of visual-linguistic explanations, and (2) Positiveness/negativeness for the specific positive/negative class. Exploiting a spatio-temporal region (tube) and an attribute as visual and linguistic explanations respectively, the explanation model is trained to predict the counterfactuality for possible combinations of multimodal information in a post-hoc manner. The optimization problem, which appears during the training/inference process, can be efficiently solved by inserting a novel neural network layer, namely the maximum subpath layer. We demonstrated the effectiveness of this method by comparison with a baseline of the action-recognition datasets extended for this task. Moreover, we provide information-theoretical insight into the proposed method.

使用视觉语言信息结合最大子路径神经网络层，实现对于多模式信息组合的反事实解释的有效解决方法，对行为识别数据集进行对比实验并提供了信息论上的解释。

通过预测视频中的反事实性达到多模态解释