English video scene semantic parsing technique based on sentence semantics and adaptive feature selection

A semantic parsing model for English video scenes based on sentence semantics and adaptive feature selection is proposed, which dynamically optimises visual features through text guidance and achieves cross modal semantic alignment.The experiment shows that the model performs well on multiple datasets: the average intersection to union ratio on ActiveNet reaches 91, exceeding other models by more than 11 points; Only 60 rounds of training on InternVid achieved an average intersection to union ratio of 90.The model parameter size is 185M, and the inference speed on RTX 3090 reaches 28.5 fps.After lightweighting, the parameter count decreased to 92M and the speed increased to 45.8 fps.This study effectively alleviates the cross modal semantic gap and feature redundancy issues, providing an efficient and accurate solution for intelligent video analysis.