  Computer vision is widely used in the fields of driverless, face recognition
and 3D reconstruction as a technology to help or replace human eye perception
images or multidimensional data through computers. Nowadays, with the
development and application of deep neural networks, the models of deep neural
networks proposed for computer vision are becoming more and more abundant, and
developers will use the already trained models on the way to solve problems,
and need to consult the relevant documents to understand the use of the model.
The class model, which creates the need to quickly and accurately find the
relevant models that you need. The automatic annotation method of visual depth
neural network proposed in this paper is based on natural language processing
technology such as semantic analysis, which realizes automatic labeling of
model application fields. In the three top international conferences on
computer vision: ICCV, CVPR and ECCV, the average correct rate of application
of the papers of 72 papers reached 90%, indicating the effectiveness of the
automatic labeling system.
