CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classification

• URL: http://arxiv.org/abs/2401.06194v1
• Date: Thu, 11 Jan 2024 13:22:38 GMT
• Title: CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classification
• Authors: Shubham Gupta, Nandini Saini, Suman Kundu, Debasis Das
• Abstract summary: CrisisKAN is a Knowledge-infused and Explainable Multimodal Attention Network that entails images and texts in conjunction with external knowledge from Wikipedia to classify crisis events. To enrich the context-specific understanding of textual information, we integrated Wikipedia knowledge using proposed wiki extraction algorithm. In order to ensure reliability, we employ a model-specific approach calledGrad-CAM that provides a robust explanation of the predictions of the proposed model.
• Score: 25.93602006155562
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pervasive use of social media has become the emerging source for real-time information (like images, text, or both) to identify various events. Despite the rapid growth of image and text-based event classification, the state-of-the-art (SOTA) models find it challenging to bridge the semantic gap between features of image and text modalities due to inconsistent encoding. Also, the black-box nature of models fails to explain the model's outcomes for building trust in high-stakes situations such as disasters, pandemic. Additionally, the word limit imposed on social media posts can potentially introduce bias towards specific events. To address these issues, we proposed CrisisKAN, a novel Knowledge-infused and Explainable Multimodal Attention Network that entails images and texts in conjunction with external knowledge from Wikipedia to classify crisis events. To enrich the context-specific understanding of textual information, we integrated Wikipedia knowledge using proposed wiki extraction algorithm. Along with this, a guided cross-attention module is implemented to fill the semantic gap in integrating visual and textual data. In order to ensure reliability, we employ a model-specific approach called Gradient-weighted Class Activation Mapping (Grad-CAM) that provides a robust explanation of the predictions of the proposed model. The comprehensive experiments conducted on the CrisisMMD dataset yield in-depth analysis across various crisis-specific tasks and settings. As a result, CrisisKAN outperforms existing SOTA methodologies and provides a novel view in the domain of explainable multimodal event classification.

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