13 citations · 23 across the 7 of their papers we have counts for
8 papers
SAM 3: Segment Anything with Concepts
Nicolas Carion, Laura Gustafson, Yuan-Ting Hu +35
We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short…
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…
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…
Probing the Mid-level Vision Capabilities of Self-Supervised Learning
Xuweiyi Chen, Markus Marks, Zezhou Cheng
Mid-level vision capabilities - such as generic object localization and 3D geometric understanding - are not only fundamental to human vision but are also crucial for many real-wor…
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…
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…