7 citations · 13 across the 9 of their papers we have counts for
11 papers
Inference-Time Scaling of Diffusion Models via Progressive Seed Pruning
Rogerio Guimaraes, Pietro Perona
Diffusion and flow-matching models dominate conditional image generation, yet inference-time scaling for these models is far less developed than for autoregressive language models.…
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…
Social Perception of Faces in a Vision-Language Model
Carina I. Hausladen, Manuel Knott, Colin F. Camerer +1
We explore social perception of human faces in CLIP, a widely used open-source vision-language model. To this end, we compare the similarity in CLIP embeddings between different te…
A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification
Markus Marks, Manuel Knott, Neehar Kondapaneni +4
Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn abo…
A Foundation Model for Cell Segmentation
Uriah Israel, Markus Marks, Rohit Dilip +11
Cells are the fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. W…
Text-image Alignment for Diffusion-based Perception
Neehar Kondapaneni, Markus Marks, Manuel Knott +2
Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. Ho…