Explaining the Unseen: Multimodal Vision-Language Reasoning for Situational Awareness in Underground Mining Disasters
Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong

TL;DR
This paper introduces MDSE, a multimodal vision-language framework designed to generate detailed textual explanations of underground disaster scenes, enhancing situational awareness in obscured and hazardous environments.
Contribution
The paper presents a novel multimodal framework with innovative attention and encoding mechanisms, along with a new dataset for underground disaster scene captioning.
Findings
MDSE outperforms existing captioning models on UMD dataset
The framework produces more accurate, contextually relevant descriptions
Experimental results demonstrate improved situational awareness capabilities
Abstract
Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine Disaster (UMD) dataset--the first image-caption corpus of real underground disaster…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Neural Network Applications · Generative Adversarial Networks and Image Synthesis
