5 citations · 13 across the 6 of their papers we have counts for
6 papers
Spatiotemporal Pyramid Flow Matching for Climate Emulation
Jeremy Andrew Irvin, Jiaqi Han, Zikui Wang +6
Generative models have the potential to transform the way we emulate Earth's changing climate. Previous generative approaches rely on weather-scale autoregression for climate emula…
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen +5
Large vision and language assistants have enabled new capabilities for interpreting natural images. These approaches have recently been adapted to earth observation data, but they…
Many-Shot In-Context Learning in Multimodal Foundation Models
Yixing Jiang, Jeremy Irvin, Ji Hun Wang +3
Large language models are effective at few-shot in-context learning (ICL). Recent advancements in multimodal foundation models have enabled unprecedentedly long context windows, pr…
CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds
Muhammad Ahmed Chaudhry, Lyna Kim, Jeremy Irvin +5
Clouds play a significant role in global temperature regulation through their effect on planetary albedo. Anthropogenic emissions of aerosols can alter the albedo of clouds, but th…
An Empirical Study of Automated Mislabel Detection in Real World Vision Datasets
Maya Srikanth, Jeremy Irvin, Brian Wesley Hill +3
Major advancements in computer vision can primarily be attributed to the use of labeled datasets. However, acquiring labels for datasets often results in errors which can harm mode…
USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery
Jeremy Irvin, Lucas Tao, Joanne Zhou +4
Large, self-supervised vision models have led to substantial advancements for automatically interpreting natural images. Recent works have begun tailoring these methods to remote s…