Dance Teaching Video Generation Algorithm Based on Long Short-Term Memory Network

Applying the method of generating dance teaching video descriptions based on deep learning to massive video retrieval and video content auditing aims to efficiently organize and manage dance teaching videos by generating semantic texts through video description methods. However, existing dance teaching video description methods lack exploration of semantic information and fail to focus on the semantic features and expressions of specific actions in the videos. To address the challenges at hand, this paper introduces a novel approach for generating descriptions of dance teaching videos by leveraging attention mechanisms and semantic guidance. In this approach, adaptive attention gated units are employed during the decoding process to combine visual features with semantic information. By guiding the model through two dedicated LSTM layers, the proposed method enhances the accuracy of word generation. To assess the efficacy of this approach, extensive experiments are conducted using appropriate datasets. The results demonstrate the significant advantages of integrating attention mechanisms and semantic guidance in dance teaching video description generation. The proposed method outperforms alternative models across multiple evaluation metrics, confirming its effectiveness in improving the overall performance of video description generation in the context of dance instruction.