562 citations · 844 across the 6 of their papers we have counts for
5 papers · 1 filter
Segment Anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi +9
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest seg…
Revisiting Weakly Supervised Pre-Training of Visual Perception Models
Mannat Singh, Laura Gustafson, Aaron Adcock +7
Model pre-training is a cornerstone of modern visual recognition systems. Although fully supervised pre-training on datasets like ImageNet is still the de-facto standard, recent st…
Masked Autoencoders Are Scalable Vision Learners
Kaiming He, Xinlei Chen, Saining Xie +3
This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and…
Fast Edge Detection Using Structured Forests
Piotr Dollár, C. Lawrence Zitnick
Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local stru…
Local Decorrelation For Improved Detection
Woonhyun Nam, Piotr Dollár, Joon Hee Han
Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on…