2.5k citations · 9.9k across the 27 of their papers we have counts for
31 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…
Benchmarking Detection Transfer Learning with Vision Transformers
Yanghao Li, Saining Xie, Xinlei Chen +3
Object detection is a central downstream task used to test if pre-trained network parameters confer benefits, such as improved accuracy or training speed. The complexity of object…
Early Convolutions Help Transformers See Better
Tete Xiao, Mannat Singh, Eric Mintun +3
Vision transformer (ViT) models exhibit substandard optimizability. In particular, they are sensitive to the choice of optimizer (AdamW vs. SGD), optimizer hyperparameters, and tra…
Fast and Accurate Model Scaling
Piotr Dollár, Mannat Singh, Ross Girshick
In this work we analyze strategies for convolutional neural network scaling; that is, the process of scaling a base convolutional network to endow it with greater computational com…
Designing Network Design Spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick +2
In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settin…
LVIS: A Dataset for Large Vocabulary Instance Segmentation
Agrim Gupta, Piotr Dollár, Ross Girshick
Progress on object detection is enabled by datasets that focus the research community's attention on open challenges. This process led us from simple images to complex scenes and f…
On Network Design Spaces for Visual Recognition
Ilija Radosavovic, Justin Johnson, Saining Xie +2
Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this wo…
TensorMask: A Foundation for Dense Object Segmentation
Xinlei Chen, Ross Girshick, Kaiming He +1
Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segm…
Panoptic Feature Pyramid Networks
Alexander Kirillov, Ross Girshick, Kaiming He +1
The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation…
Rethinking ImageNet Pre-training
Kaiming He, Ross Girshick, Piotr Dollár
We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization. The results are no worse t…
Panoptic Segmentation
Alexander Kirillov, Kaiming He, Ross Girshick +2
We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pi…
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…
Learning Features by Watching Objects Move
Deepak Pathak, Ross Girshick, Piotr Dollár +2
This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues ca…
Feature Pyramid Networks for Object Detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick +3
Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning object detectors have avoided pyramid representati…
Aggregated Residual Transformations for Deep Neural Networks
Saining Xie, Ross Girshick, Piotr Dollár +2
We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformat…
A MultiPath Network for Object Detection
Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin +4
The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical imag…
Learning to Refine Object Segments
Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert +1
Object segmentation requires both object-level information and low-level pixel data. This presents a challenge for feedforward networks: lower layers in convolutional nets capture…
Unsupervised Learning of Edges
Yin Li, Manohar Paluri, James M. Rehg +1
Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervi…
Semantic Amodal Segmentation
Yan Zhu, Yuandong Tian, Dimitris Mexatas +1
Common visual recognition tasks such as classification, object detection, and semantic segmentation are rapidly reaching maturity, and given the recent rate of progress, it is not…