Despite neural networks (NN) have been widely applied in various fields and generally outperforms humans, they still lack interpretability to a certain extent, and humans are unable to intuitively understand the decision logic of NN. This also hinders the knowledge interaction between humans and NN, preventing humans from getting involved to give direct guidance when NN's decisions go wrong. While recent research in explainable AI has achieved interpretability of NN from various perspectives, it has not yet provided effective methods for knowledge exchange between humans and NN. To address this problem, we constructed a two-way interaction interface that uses structured representations of visual concepts and their relationships as the "language" for knowledge exchange between humans and NN. Specifically, NN provide intuitive reasoning explanations to humans based on the class-specific structural concepts graph (C-SCG). On the other hand, humans can modify the biases present in the C-SCG through their prior knowledge and reasoning ability, and thus provide direct knowledge guidance to NN through this interface. Through experimental validation, based on this interaction interface, NN can provide humans with easily understandable explanations of the reasoning process. Furthermore, human involvement and prior knowledge can directly and effectively contribute to enhancing the performance of NN.

翻译：尽管神经网络（NN）已被广泛应用于各个领域且通常表现优于人类，但其仍存在一定程度的不可解释性，人类无法直观理解NN的决策逻辑。这阻碍了人类与NN之间的知识交互，使人类在NN决策出现偏差时无法介入并提供直接指导。虽然近期可解释人工智能研究已从多角度实现了NN的可解释性，但尚未提供有效的人类-NN知识交换方法。针对此问题，我们构建了一个双向交互接口，采用结构化表示的视觉概念及其关系作为人类与NN知识交换的"语言"。具体而言，NN基于类别特定结构概念图（C-SCG）向人类提供直观的推理解释。另一方面，人类可凭借先验知识和推理能力修正C-SCG中存在的偏差，并通过该接口向NN提供直接知识指导。实验验证表明：基于该交互接口，NN能够为人类提供易于理解的推理过程解释；同时，人类介入和先验知识可直接有效地提升NN性能。