Optimization of multimodal large-scale model graph matching algorithms with integrated attention mechanisms

Abstract

With ever-growing conception of artificial intelligence and smart algorithms, the progress of complicated relationship modeling and combination technologies of multi-source information has become rapid. Graph matching as a key question in the structured data analysis and inter-modal semantic alignment has been of great importance in knowledge graph construction, cross-modal retrieval and intelligent perception. By overcoming the shortcomings of conventional graph matching techniques, including poor multimodal feature representation, poor node importance characterization and limited resilience in complex structural settings, the given research suggests an optimized multimodal graph matching model including attention schemes. It has been shown in experimental results on several benchmark datasets that the proposed algorithm satisfies: - a higher discriminative capacity and stability when it comes to matching features across heterogeneous modalities (structural, semantic, visual); - a multimodal attention mechanism that can participate in adaptive models to model the contribution of various modalities; - a graph attention message-passing mechanism to add weight to the representation of higher-order structural relations among nodes Compared to more comparative methods, the proposed algorithm is demonstrated to outperform on a variety of benchmark datasets in a variety of metrics of matching accuracy, recall, It has notably good generalization and robust in big graphs and intricate semantic situations. The results confirm the efficiency of multi-modal attention multi-characteristic collaborative model on graph matching operations, which offers another technical direction to multi-modal matching dilemmas on more intricate graph arrangements.