CV large model refers to a deep learning Xi model that uses a large number of parameters and data for pre-training in the field of computer vision, usually based on the Transformer structure, with the purpose of Xi learning a general visual feature representation, which can be used for different downstream tasks, such as image classification, object detection, semantic segmentation, etc. The advantages of CV large models are that they can improve the generalization ability and performance of the model, shorten the cycle of model development and deployment, and reduce the cost of labeled data.
Application fields of CV large models.
Source**: Joint Research Industry Consulting (Joint Research Network).
Variational Autoencoder (VAE) and Generative Adversarial Network (GANe) have opened up a new path for image generation and processing. Recently, visual converters have treated image processing as text processing through a multi-head attention mechanism, splitting images into local graphs, and then performing vector embedding and correlation calculations. This method does not use prior knowledge of images, making it more generalizable and performance-improving.
The development history of the CV model.
Source**: Joint Research Industry Consulting (Joint Research Network).
The market size of China's AI industry driven by the CV model will be 189 in 2022500 million yuan, in the embryonic period of the beginning of the industry. With the maturity of the technology of the CV model and the broadening of the commercialization scenario, it is expected that the market size of the AL industry based on the CV model will enter a period of rapid growth in the future, reaching 508400 million yuan.
2020-2027 China's CV large model market size** and growth rate.
Source**: Joint Research Industry Consulting (Joint Research Network).
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