Utkarsh Byahut
Computer science
Hometown: Patna, Bihar, India
Graduation date: Spring 2026
FURI | Spring 2026
Few-Shot Disaster Classification via Causal Disentanglement and Cross-Modal Flattening
Rapid situational awareness is vital for public safety during emergencies, yet current disaster classification models require massive datasets to function reliably. In the early stages of a unique or localized crisis, labeled data is often non-existent, and traditional deep learning models fail to categorize these events accurately with limited samples. This “data hunger” creates a critical bottleneck, preventing intelligent systems from providing immediate support when every second counts.
To address this, we propose a few-shot multimodal learning framework for disaster classification. Our approach utilizes causal disentanglement to separate invariant disaster features from noise and cross-modal flattening to align information across different data types (such as text and images). By identifying the underlying causal mechanisms of a crisis rather than relying on volume-heavy pattern matching, this framework enables “off-the-shelf” models to adapt to new disaster types with minimal supervision. This research contributes to national resilience by ensuring emergency systems remain accurate and deployable even in data-poor scenarios.
Mentor: Huan Liu
It’s hip to be square.
Students presenting projects at the Fulton Forge Student Research Expo are encouraged to download this personal QR code and include it within your poster. This allows expo attendees to explore more about your project and about you in the future.
Right click the image to save it to your computer.