視覚と言語によるナビゲーションの説明手法の評価
Evaluating Explanation Methods for Vision-and-Language Navigation
視覚と言語によるナビゲーション（VLN）モデルの説明手法を忠実性の観点から定量的に評価するベンチマークを構築し、逐次意思決定におけるテキスト説明を測定する新たな消去ベースの評価パイプラインを提案した。
著者: Guanqi Chen, Lei Yang, Guanhua Chen, Jia Pan
分類: cs.RO, cs.CV
原文アブストラクト
The ability to navigate robots with natural language instructions in an unknown environment is a crucial step for achieving embodied artificial intelligence (AI). With the improving performance of deep neural models proposed in the field of vision-and-language navigation (VLN), it is equally interesting to know what information the models utilize for their decision-making in the navigation tasks. To understand the inner workings of deep neural models, various explanation methods have been developed for promoting explainable AI (XAI). But they are mostly applied to deep neural models for image or text classification tasks and little work has been done in explaining deep neural models for VLN tasks. In this paper, we address these problems by building quantitative benchmarks to evaluate explanation methods for VLN models in terms of faithfulness. We propose a new erasure-based evaluation pipeline to measure the step-wise textual explanation in the sequential decision-making setting. We evaluate several explanation methods for two representative VLN models on two popular VLN datasets and reveal valuable findings through our experiments.
