Automatic annotation of visual deep neural networks
This addresses the need for developers to efficiently locate and understand models in computer vision, though it appears incremental as it applies existing NLP techniques to a new domain.
The paper tackles the problem of quickly finding relevant pre-trained deep neural network models for computer vision by proposing an automatic annotation method based on natural language processing, achieving an average correct rate of 90% on 72 papers from top conferences.
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.