D. Paudel

This work presents EarthMind, a novel vision-language framework for multi-granular and multi-sensor EO data understanding and outperforms existing methods on multiple public EO benchmarks, showcasing its potential to handle both multi-granular and multi-sensor challenges in a unified framework.

iARCS transforms 3D scene generation by ensuring that synthetic environments meet essential functional constraints while maintaining diversity and realism.

Over 1,500 submissions revealed stark differences in model performance across diverse domains, highlighting the challenges of generalizing egocentric video understanding.

Image-size agnostic vision transformers are now a practical reality, thanks to a new self-supervised pretraining method that maintains constant computational cost regardless of input resolution.

Autonomous vehicles can now better identify the unexpected, thanks to a new method that boosts out-of-distribution detection by up to 20% without retraining.

Forget expensive teacher models and manual labeling: a base VLM paired with OpenStreetMap data can annotate itself for remote sensing tasks, achieving state-of-the-art performance at a fraction of the cost.

EarthMind demonstrates that hierarchical cross-modal attention across optical and SAR data significantly boosts MLLM performance on Earth Observation tasks, outperforming models limited to single-sensor inputs.