2 citations · 5 across the 12 of their papers we have counts for
6 papers · 1 filter
New York Smells: A Large Multimodal Dataset for Olfaction
Ege Ozguroglu, Junbang Liang, Ruoshi Liu +6
While olfaction is central to how animals perceive the world, this rich chemical sensory modality remains largely inaccessible to machines. One key bottleneck is the lack of divers…
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…
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…
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…
Muscles in Action
Mia Chiquier, Carl Vondrick
Human motion is created by, and constrained by, our muscles. We take a first step at building computer vision methods that represent the internal muscle activity that causes motion…
The Boombox: Visual Reconstruction from Acoustic Vibrations
Boyuan Chen, Mia Chiquier, Hod Lipson +1
Interacting with bins and containers is a fundamental task in robotics, making state estimation of the objects inside the bin critical. While robots often use cameras for state est…