GMDA:基于 GCN 的多模式域自适应,用于实时灾害检测

Yingdong Gou, Kexin Wang, Siwen Wei, Changxin Shi
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引用次数: 0

摘要

如今,随着社交媒体作为快速沟通手段的迅速发展,实时灾害信息通过这些平台得到广泛传播。确定哪些实时和多模式灾害信息能有效支持人道主义援助已成为一大挑战。本文提出了一种新颖的端到端模型,命名为基于 GCN 的多模态域适应(GMDA),由三个基本模块组成:基于 GCN 的特征提取模块、基于注意力的融合模块和 MMD 域适应模块。基于 GCN 的特征提取模块通过 GCN 整合文本和图像表征,而基于注意力的融合模块则利用注意力机制合并这些多模态表征。最后,利用 MMD 域适应模块,通过计算跨域的最大平均差异,减轻 GMDA 对源域事件的依赖。我们提出的模型经过了广泛的评估,与最先进的多模态域适应模型相比,在 F1 分数和方差稳定性方面表现出了卓越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GMDA: GCN-Based Multi-Modal Domain Adaptation for Real-Time Disaster Detection
Nowadays, with the rapid expansion of social media as a means of quick communication, real-time disaster information is widely disseminated through these platforms. Determining which real-time and multi-modal disaster information can effectively support humanitarian aid has become a major challenge. In this paper, we propose a novel end-to-end model, named GCN-based Multi-modal Domain Adaptation (GMDA), which consists of three essential modules: the GCN-based feature extraction module, the attention-based fusion module and the MMD domain adaptation module. The GCN-based feature extraction module integrates text and image representations through GCNs, while the attention-based fusion module then merges these multi-modal representations using an attention mechanism. Finally, the MMD domain adaptation module is utilized to alleviate the dependence of GMDA on source domain events by computing the maximum mean discrepancy across domains. Our proposed model has been extensively evaluated and has shown superior performance compared to state-of-the-art multi-modal domain adaptation models in terms of F1 score and variance stability.
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