10 citations · 10 across the 5 of their papers we have counts for
6 papers
Teaching Humans Subtle Differences with DIFFusion
Mia Chiquier, Orr Avrech, Yossi Gandelsman +3
Scientific expertise often requires recognizing subtle visual differences that remain challenging to articulate even for domain experts. We present a system that leverages generati…
Towards LLM Agents for Earth Observation
Chia Hsiang Kao, Wenting Zhao, Cheryl Lam +10
Earth Observation (EO) provides critical planetary data for environmental monitoring, disaster management, climate science, and other scientific domains. In this work we ask: Are A…
DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery
Utkarsh Mall, Cheng Perng Phoo, Mia Chiquier +3
Visual data is used in numerous different scientific workflows ranging from remote sensing to ecology. As the amount of observation data increases, the challenge is not just to mak…
How Video Meetings Change Your Expression
Sumit Sarin, Utkarsh Mall, Purva Tendulkar +1
Do our facial expressions change when we speak over video calls? Given two unpaired sets of videos of people, we seek to automatically find spatio-temporal patterns that are distin…
Evolving Interpretable Visual Classifiers with Large Language Models
Mia Chiquier, Utkarsh Mall, Carl Vondrick
Multimodal pre-trained models, such as CLIP, are popular for zero-shot classification due to their open-vocabulary flexibility and high performance. However, vision-language models…
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
Utkarsh Mall, Cheng Perng Phoo, Meilin Kelsey Liu +3
We introduce a method to train vision-language models for remote-sensing images without using any textual annotations. Our key insight is to use co-located internet imagery taken o…