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This paper explores post-disaster analytics using multimodal deep learningmodels trained with curriculum learning method. Studying post-disasteranalytics is important as it plays a crucial role in mitigating the impact ofdisasters by providing timely and accurate insights into the extent of damageand the allocation of resources. We propose a curriculum learning strategy toenhance the performance of multimodal deep learning models. Curriculum learningemulates the progressive learning sequence in human education by training deeplearning models on increasingly complex data. Our primary objective is todevelop a curriculum-trained multimodal deep learning model, with a particularfocus on visual question answering (VQA) capable of jointly processing imageand text data, in conjunction with semantic segmentation for disaster analyticsusing theFloodNet\footnote{https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021}dataset. To achieve this, U-Net model is used for semantic segmentation andimage encoding. A custom built text classifier is used for visual questionanswering. Existing curriculum learning methods rely on manually defineddifficulty functions. We introduce a novel curriculum learning approach termedDynamic Task and Weight Prioritization (DATWEP), which leverages agradient-based method to automatically decide task difficulty during curriculumlearning training, thereby eliminating the need for explicit difficultycomputation. The integration of DATWEP into our multimodal model showsimprovement on VQA performance. Source code is available athttps://github.com/fualsan/DATWEP.

Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery