  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.
