Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification

Few-Shot Learning has had a significant influence on how people live, work, and learn. Physical education is a requirement for a college diploma. Sports management systems, which focus on data collection, organization, and analysis, as well as timeliness and guidance, are one of the current challenges in the field of physical education at the country’s top colleges and universities. The amount of sex in the room is minimal. Time is money when it comes to making college sports decisions, and this paper uses data from physical fitness tests to illustrate this point. Use Few-Shot Learning technology to extract relevant data from the data, allowing teachers to provide more scientific and effective guidance and suggestions to students. The design and implementation of this paper collect data from physical fitness tests in real-time using mobile edge computing, analyze the data, and display the results using machine learning technology, which mines deep features and displays analysis results, can be used to evaluate students’ physical fitness. The data and information in the physical fitness analysis system are more readable and time-saving, allowing students to better understand their true level of physical fitness. Because of the results of data mining, teachers can provide more specific guidance and recommendations for each student’s physical characteristics.

Section: Research and Construction Of A Decision Support System For U...mentioning

confidence: 99%

College Sports Decision-Making Algorithm Based on Machine Few-Shot Learning and Health Information Mining Technology

Zhang

2022

“…The definition of machine learning is that a computer program learns from experience (experiences, E) to solve a certain task (task, T) and perform a certain performance measure (performance, P). Performance P on task T improves with experience E. While Few-Shot learning is a class of machine learning problems (specified by E, T, and P), where E contains little supervision information for the target T. Specific to the image classification [ 9 , 10 ] task based on Few-Shot learning, T is the image classification task, E is a small number of labeled pictures and prior knowledge used for the classification task, and the performance measure P is the accuracy rate. According to the definition of machine learning, the classification task should be to improve the experience E based on the measured accuracy, while the few labeled images are fixed.…”

Section: Few-shot Learning Based On Contrastive Average Lossmentioning

confidence: 99%

Dance Fitness Action Recognition Method Based on Contour Image Spatial Frequency Domain Features and Few-Shot Learning

Chi

2022

In recent years, the research work of artificial intelligence technology has progressed rapidly, and various classic Few-Shot learning models have achieved unprecedented success in many artificial intelligence application fields. These include face recognition, object classification detection and tracking, speech recognition, and natural language processing, which greatly facilitate our lives. This paper aims to identify dance fitness movements based on contour image spatial frequency domain features and Few-Shot learning technology. This paper proposes a Few-Shot learning method based on contrastive average loss for Few-Shot learning. This method makes the learned model more representative by improving the loss function and performing a normalization process, and it proposes a feature extraction algorithm that combines improved LBP and HOG for action recognition technology. The experimental results show that the recognition accuracy of the algorithm in this paper is 93.10%, 90.30%, and 92.70% for walking, opening hands, and running, respectively. This illustrates the effectiveness of the fusion feature algorithm.

“…With the innovation of information technology and the constant replacement of computing power of hardware devices, artificial intelligence [ 9 – 11 ] is developing vigorously in the information society, and the field of machine learning represented by generative model continues to attract researchers' attention.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Recognition and Analysis of Gait Contour of Dance Movements Based on Generative Adversarial Networks

Ren

Park

2022

With the generation of images, videos, and other data, how to identify the gait of the action in the video has gradually become the focus of research. Aiming at the problems of complex and changeable movements, strong coherence, and serious occlusion in dance video images, this paper proposes a dynamic recognition model of gait contour of dance movements based on GAN (generative adversarial networks). GAN method is used to convert the gait diagrams in any state into a group of gait diagrams in normal state with multiple angles, which are arranged in turn. In order to retain as much original feature information as possible, multiple loss strategy is adopted to optimize the network, increase the distance between classes, and reduce the distance within classes. Experimental results show that the average recognition rates of this model at 50°, 90°, and 120°are 93.24, 98.24, and 97.93, respectively, which shows that the recognition accuracy of dance movement recognition method is high. And this method can effectively improve the dynamic recognition of gait contour of dance movements.