Volleyball training video classification description using the BiLSTM fusion attention mechanism.

ABSTRACT: This study aims to explore methods for classifying and describing volleyball training videos using deep learning techniques. By developing an innovative model that integrates Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanisms, referred to BiLSTM-Multimodal Attention Fusion Temporal Classification (BiLSTM-MAFTC), the study enhances the accuracy and efficiency of volleyball video content analysis. Initially, the model encodes features from various modalities into feature vectors, capturing different types of information such as positional and modal data. The BiLSTM network is then used to model multi-modal temporal information, while spatial and channel attention mechanisms are incorporated to form a dual-attention module. This module establishes correlations between dif

SUBMITTER: Ruiye Z 

PROVIDER: S-EPMC11320147 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature