6 papers
Diffusion-Based Action Recognition Generalizes to Untrained Domains
Rogerio Guimaraes, Frank Xiao, Pietro Perona +1
Humans can recognize the same actions despite large context and viewpoint variations, such as differences between species (walking in spiders vs. horses), viewpoints (egocentric vs…
Representational Difference Explanations
Neehar Kondapaneni, Oisin Mac Aodha, Pietro Perona
We propose a method for discovering and visualizing the differences between two learned representations, enabling more direct and interpretable model comparisons. We validate our m…
SAVeD: Learning to Denoise Low-SNR Video for Improved Downstream Performance
Suzanne Stathatos, Michael Hobley, Pietro Perona +1
Low signal-to-noise ratio videos -- such as those from underwater sonar, ultrasound, and microscopy -- pose significant challenges for computer vision models, particularly when pai…
Representational Similarity via Interpretable Visual Concepts
Neehar Kondapaneni, Oisin Mac Aodha, Pietro Perona
How do two deep neural networks differ in how they arrive at a decision? Measuring the similarity of deep networks has been a long-standing open question. Most existing methods pro…
A Rapid Test for Accuracy and Bias of Face Recognition Technology
Manuel Knott, Ignacio Serna, Ethan Mann +1
Measuring the accuracy of face recognition (FR) systems is essential for improving performance and ensuring responsible use. Accuracy is typically estimated using large annotated d…
Learning Keypoints for Multi-Agent Behavior Analysis using Self-Supervision
Daniel Khalil, Christina Liu, Pietro Perona +2
The study of social interactions and collective behaviors through multi-agent video analysis is crucial in biology. While self-supervised keypoint discovery has emerged as a promis…