Recurrent generative adversarial networks for unsupervised WCE video summarization

The analysis of generative adversarial network in sports education based on deep learning

The importance of mental health is increasingly emphasized in modern society. The assessment of mental health qualities among college and university students as the future workforce holds significant significance. Therefore, this study, aiming to streamline the process of writing quality evaluations and enhance the fairness of assessment comments, explores the use of Generative Adversarial Network (GAN) technology in deep learning to evaluate the mental health qualities of college and university students through the unique avenue of sports. Firstly, GAN and Sequence Generative Adversarial Network (SeqGAN) models are introduced. Secondly, GAN is employed to construct a model for generating evaluation texts, encompassing the construction of a generator and discriminator, along with the introduction of a reward function. Finally, the constructed model is utilized to train on evaluation texts related to the mental health qualities of college and university students engaged in sports, validating the effectiveness of the model. The results indicate: (1) The pre-training of the generator in the constructed text generation model stabilizes after the 10th epoch. In contrast, the pre-training of the discriminator gradually stabilizes after the 35th epoch, demonstrating overall good training effectiveness. (2) When the generator's update speed surpasses that of the discriminator, the model's loss does not converge. However, with a reduction in the ratio of rounds between the two, there is a noticeable improvement in the convergence of the model. (3) The mean score of adaptability quality is the highest among the four indicators, suggesting a strong correlation between comment generation and adaptability quality. The results validate the effectiveness of the proposed text generation model in semantic control. This study aims to advance the level of mental health education among college and university students in the sports domain, providing theoretical references for enhancing the effectiveness of quality education assessments in other subjects as well.

Video summarization using deep learning techniques: a detailed analysis and investigation

One of the critical multimedia analysis problems in today’s digital world is video summarization (VS). Many VS methods have been suggested based on deep learning methods. Nevertheless, These are inefficient in processing, extracting, and deriving information in the minimum amount of time from long-duration videos. Detailed analysis and investigation of numerous deep learning approach accomplished to determine root of problems connected with different deep learning methods in identifying and summarizing the essential activities in such videos. Various deep learning techniques have been investigated and examined to detect the event and summarization capability for detecting and summarizing multiple activities. Keyframe selection Event detection, categorization, and the activity feature summarization correspond to each activity. The limitations related to each category are also discussed in depth. Concerns about detecting low activity using the deep network on various types of public datasets are also discussed. Viable strategies are suggested to evaluate and improve the generated video summaries on such datasets. Moreover, Potential recommended applications based on literature are listed out. Various deep learning tools for experimental analysis have also been discussed in the paper. Future directions are presented for further exploration of research in VS using deep learning strategies.