4.3k citations · 6.3k across the 14 of their papers we have counts for
14 papers
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…
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten +3
When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings. Ex…
Actions and Attributes from Wholes and Parts
Georgia Gkioxari, Ross Girshick, Jitendra Malik
We investigate the importance of parts for the tasks of action and attribute classification. We develop a part-based approach by leveraging convolutional network features inspired…
Hypercolumns for Object Segmentation and Fine-grained Localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick +1
Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as feature representation. However, the information in this layer may be to…