@article{Lu2025, 
author = {Jing Lu and Chunlei Wu and Leiquan Wang and Ran Li and Xiuxuan Shen},
title = {Dual-Modality Integration Attention with Graph-Based Feature Extraction for Visual Question and Answering},
year = {2025},
journal = {Tsinghua Science and Technology},
volume = {30},
number = {5},
pages = {2133-2145},
keywords = {Visual Question and Answering (VQA), Graph-based Feature Extraction (GFE), Dual-modality Integration Attention (DIA), composite loss},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010093},
doi = {10.26599/TST.2024.9010093},
abstract = {Visual Question and Answering (VQA) has garnered significant attention as a domain that requires the synthesis of visual and textual information to produce accurate responses. While existing methods often rely on Convolutional Neural Networks (CNNs) for feature extraction and attention mechanisms for embedding learning, they frequently fail to capture the nuanced interactions between entities within images, leading to potential ambiguities in answer generation. In this paper, we introduce a novel network architecture, Dual-modality Integration Attention with Graph-based Feature Extraction (DIAGFE), which addresses these limitations by incorporating two key innovations: a Graph-based Feature Extraction (GFE) module that enhances the precision of visual semantics extraction, and a Dual-modality Integration Attention (DIA) mechanism that efficiently fuses visual and question features to guide the model towards more accurate answer generation. Our model is trained with a composite loss function to refine its predictive accuracy. Rigorous experiments on the VQA2.0 dataset demonstrate that DIAGFE outperforms existing methods, underscoring the effectiveness of our approach in advancing VQA research and its potential for cross-modal understanding.}
}