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.

通过构建一个双向交互界面，利用结构化的视觉概念及其关系作为人与神经网络之间的知识交流的 “语言”，研究在解释性人工智能领域取得了一定成果，但目前尚未提供人与神经网络之间的有效方法，以解决神经网络的可解释性和人机知识交互之间的问题。实验证明，通过该交互界面，神经网络能够向人类提供易于理解的推理解释过程，并且人类的参与和先验知识能够直接有效地提升神经网络的性能。