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CrisisKAN

Code repository of paper "CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classification" published in ECIR 2024

Gupta and Saini et al. "CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classification. 2024."

Preprint: https://arxiv.org/abs/2401.06194

To cite the paper:

@misc{gupta2024crisiskan,
      title={CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classification}, 
      author={Shubham Gupta and Nandini Saini and Suman Kundu and Debasis Das},
      year={2024},
      eprint={2401.06194},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

CrisisMMD Dataset

• Website: https://crisisnlp.qcri.org/crisismmd
• Dataset: https://crisisnlp.qcri.org/data/crisismmd/CrisisMMD_v2.0.tar.gz

Run the model

• Setup the environemnt and download the dataset using script bash setup.sh
• Run the pipeline with bash train_debug.sh tensorboard --logdir=runs/Mar15_16-43-20_v009.ib.bridges2.psc.edu --port=41225 =======

MultiModal Disaster Response Agent

Overview

This repository contains the implementation of a MultiModal Crisis Classification Model that integrates text and images for disaster response analysis. The model is based on the research paper "Towards MultiModal Disaster Response Agents" and is designed to extract relevant disaster-related information from social media platforms, particularly Twitter.

Problem Statement

The increasing frequency of natural disasters necessitates efficient and real-time emergency response. While social media provides real-time data, extracting relevant crisis-related information remains challenging. Traditional text-based classification models (e.g., AIDR) and image-based models have limitations. This project aims to enhance situational awareness by leveraging multimodal learning that fuses text and images.

Approach

• The project uses the CrisisMMD dataset, a human-labeled multimodal dataset for disaster classification.
• We experiment with multimodal fusion techniques to improve disaster relevance classification, severity assessment, and humanitarian categorization.
• Initially, we tested a baseline multimodal model without external knowledge infusion, achieving a training accuracy of 92.8% but validation/test accuracy of ~70%, indicating overfitting.
• Future work will integrate Visual Language Models (VLMs) for richer image-text interactions and Named Entity Recognition (NER) for location extraction.

Features

• Multimodal Data Processing: Leverages both text and image modalities for crisis classification.
• Crisis Classification Tasks:
  • Task 1: Identifies whether a social media post is informative or not.
  • Task 2: Categorizes the type of crisis-related information (e.g., injured people, infrastructure damage, missing persons, etc.).
  • Task 3: Assesses damage severity (severe, mild, or little/no damage).
• Guided Cross Attention Mechanism: Enhances feature fusion between text and image modalities.
• Deep Learning Models Used:
  • Text Processing: ELECTRA Transformer Encoder
  • Image Processing: DenseNet-based CNN

Dataset

The model is trained on CrisisMMD, a dataset of Twitter posts with text and image pairs from real-world disasters such as:

• Hurricane Irma, Hurricane Harvey, Hurricane Maria
• Mexico Earthquake, California Wildfires, Iraq-Iran Earthquake
• Sri Lanka Floods

Preprocessing steps include:

• Text Cleaning: Removal of URLs, special characters, and redundant metadata.
• Image Processing: Resizing to a standard format.
• Train-Test Split: The dataset is divided into train, validation, and test sets to ensure robust evaluation.

Installation (To be updated)

To set up the project environment, install dependencies:

pip install -r requirements.txt

Usage (To be updated)

Run the model using the provided script:

python train.py --config config.yaml

Results & Observations

• Training Accuracy: 92.8%
• Validation Accuracy: ~68%
• Test Accuracy: ~70%
• Key Challenges: Overfitting, lack of external knowledge infusion, feature extraction inefficiencies.
• Next Steps: Integration of VLMs for image interpretation and NER for location extraction.

Future Work

• Improve multimodal feature alignment.
• Integrate VLMs for richer image context.
• Use NER to extract and map disaster locations.
• Enhance generalization across different crisis types.

Contributors

• Christine N. Muthee
• Keriane L. Nzabampema

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