IMAGE RECOGNITION ALGORITHMS WITH MULTISCALE FEATURES AND META-LEARNING
In computer vision and image recognition technology,the recognition effect of convolutional neural network methods varies with the change of input resolution.Multiscale feature learning can combine the accuracy and details of an image,combining the information of multiple scales of the image for analysis.Meta-learning allows computers to simulate the human brain and learn how to learn,which allows for more efficient and flexible image classification.Therefore,combining multi-scale features and meta-learning for image recognition algorithm research has high research value.In this study,we generated input images with different resolutions by inflated convolution,used meta-learning to generate convolutional weights of neural networks with different input resolutions,and used knowledge distillation for models with different input resolutions.
国家科技期刊平台
NSTL
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