Semantic Distance-Aware Cross-Modal Attention Mechanism for Video Question Answering

Precise measurement and quantification of semantic relationships between visual and textual data streams is essential for video-based measurement systems, particularly in automated video inspection and analysis applications. Current measurement methodologies face significant challenges in calibrating and aligning semantic distributions across different measurement modalities, resulting in reduced measurement accuracy and system reliability. This article presents a novel measurement approach called the semantic distance-aware cross-modal attention mechanism (SDCAM), which introduces a cross-modal calibration function to quantify semantic disparities between visual and textual features. The proposed system employs the query text as a reference standard and utilizes semantic relative distance as a measurement metric, enabling precise cross-modal calibration and measurement. Building on SDCAM, we develop an advanced video measurement system that achieves multiscale semantic alignment through a three-stage measurement process: collaborative representation measurement (CRM), hierarchical feature aggregation (HFA), and contextual measurement synthesis (CMS). This approach generates comprehensive contextual measurements tailored to specific query parameters, significantly improving measurement accuracy across diverse video analysis tasks. Experiments conducted across four widely used video question answering (VideoQA) datasets demonstrate the superiority of the proposed model over existing state-of-the-art methods, while reducing parameters by 62%. The system’s effectiveness is further validated through integration testing with video retrieval and highlight detection tasks, where it improves measurement precision by 2% compared to baseline systems, while maintaining this performance advantage despite a 42% reduction in model parameters.