2.5k citations · 9.9k across the 27 of their papers we have counts for
6 papers · 2 filters
Data Distillation: Towards Omni-Supervised Learning
Ilija Radosavovic, Piotr Dollár, Ross Girshick +2
We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabe…
Learning to Segment Every Thing
Ronghang Hu, Piotr Dollár, Kaiming He +2
Most methods for object instance segmentation require all training examples to be labeled with segmentation masks. This requirement makes it expensive to annotate new categories an…
Focal Loss for Dense Object Detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick +2
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. I…
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal, Piotr Dollár, Ross Girshick +6
Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and developme…
Detecting and Recognizing Human-Object Interactions
Georgia Gkioxari, Ross Girshick, Piotr Dollár +1
To understand the visual world, a machine must not only recognize individual object instances but also how they interact. Humans are often at the center of such interactions and de…
Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár +1
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously genera…