Facial recognition tasks aim to automatically detect, recognize, and verify facial features through computer vision and pattern recognition technology. They have been widely used in various tasks, such as security monitoring and identity authentication. Thanks to the rapid development of machine learning...

Facial recognition has always been a focal point of computer vision research, and its goal is to build a model to distinguish between different individual identities. Most of the early face recognition algorithms relied on manual features, such as texture, shape, edge, local binary pattern, etc. However,...

Crop diseases have an essential impact on food supply and agricultural productivity. Developing quick and automated technologies for crop disease diagnosis, therefore becomes crucial. Early identification of crop diseases mainly relied on field surveys by technicians, which was labor-intensive. Field...

There are some deficiencies in the current Convolutional Neural Network (CNN) system. Some deep and complex CNN models take a long time to train, requiring a lot of computing resources and time for the training. At the same time, some CNN models may have overfitting problems, resulting in a decline in...

Convolutional Neural Networks (CNNs) have witnessed widespread adoption in the domain of image classification, while deep neural networks have been developed to tackle more intricate tasks. In the experimental investigation, a remarkable downward trend in GPU utilization was observed as the batch size...

Image classification is one of the important classifications in the field of computer vision, and the development of deep learning models has brought historic breakthroughs to the development of image classification. Transformer model, as a powerful sequence modeling tool, has achieved great success...