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• W4280585398 abstract "Information residing in multiple modalities (e.g., text, image) of social media posts can jointly provide more comprehensive and clearer insights into an ongoing emergency. To identify information valuable for humanitarian aid from noisy multimodal data, we first clarify the categories of humanitarian information, and define a multi-label multimodal humanitarian information identification task, which can adapt to the label inconsistency issue caused by modality independence while maintaining the correlation between modalities. We proposed a Multimodal Humanitarian Information Identification Model that simultaneously captures the Correlation and Independence between modalities (CIMHIM). A tailor-made dataset containing 4,383 annotated text-image pairs was built to evaluate the effectiveness of our model. The experimental results show that CIMHIM outperforms both unimodal and multimodal baseline methods by at least 0.019 in macro-F1 and 0.022 in accuracy. The combination of OCR text, object-level features, and the decision rule based on label correlations enhances the overall performance of CIMHIM. Additional experiments on a similar dataset (CrisisMMD) also demonstrate the robustness of CIMHIM. The task, model, and dataset proposed in this study contribute to the practice of leveraging multimodal social media resources to support effective emergency response." @default.
• W4280585398 created "2022-05-22" @default.
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• W4280585398 date "2022-07-01" @default.
• W4280585398 modified "2023-10-16" @default.
• W4280585398 title "Identifying humanitarian information for emergency response by modeling the correlation and independence between text and images" @default.
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• W4280585398 doi "https://doi.org/10.1016/j.ipm.2022.102977" @default.
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