167 citations · 635 across the 15 of their papers we have counts for
18 papers
R-MAE: Regions Meet Masked Autoencoders
Duy-Kien Nguyen, Vaibhav Aggarwal, Yanghao Li +4
In this work, we explore regions as a potential visual analogue of words for self-supervised image representation learning. Inspired by Masked Autoencoding (MAE), a generative pre-…
Point-Level Region Contrast for Object Detection Pre-Training
Yutong Bai, Xinlei Chen, Alexander Kirillov +2
In this work we present point-level region contrast, a self-supervised pre-training approach for the task of object detection. This approach is motivated by the two key factors in…
SLIP: Self-supervision meets Language-Image Pre-training
Norman Mu, Alexander Kirillov, David Wagner +1
Recent work has shown that self-supervised pre-training leads to improvements over supervised learning on challenging visual recognition tasks. CLIP, an exciting new approach to le…
Mask2Former for Video Instance Segmentation
Bowen Cheng, Anwesa Choudhuri, Ishan Misra +3
We find Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline. In this re…
Recognizing Scenes from Novel Viewpoints
Shengyi Qian, Alexander Kirillov, Nikhila Ravi +4
Humans can perceive scenes in 3D from a handful of 2D views. For AI agents, the ability to recognize a scene from any viewpoint given only a few images enables them to efficiently…
Masked-attention Mask Transformer for Universal Image Segmentation
Bowen Cheng, Ishan Misra, Alexander G. Schwing +2
Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics…
Per-Pixel Classification is Not All You Need for Semantic Segmentation
Bowen Cheng, Alexander G. Schwing, Alexander Kirillov
Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation is handled with an alternative mask classification…
End-to-End Object Detection with Transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve +3
We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand…
PointRend: Image Segmentation as Rendering
Alexander Kirillov, Yuxin Wu, Kaiming He +1
We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- a…
Exploring Randomly Wired Neural Networks for Image Recognition
Saining Xie, Alexander Kirillov, Ross Girshick +1
Neural networks for image recognition have evolved through extensive manual design from simple chain-like models to structures with multiple wiring paths. The success of ResNets an…
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…
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…
Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation
Omid Hosseini Jafari, Oliver Groth, Alexander Kirillov +2
This paper addresses the task of designing a modular neural network architecture that jointly solves different tasks. As an example we use the tasks of depth estimation and semanti…
Global Hypothesis Generation for 6D Object Pose Estimation
Frank Michel, Alexander Kirillov, Eric Brachmann +4
This paper addresses the task of estimating the 6D pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) Compute local feat…
InstanceCut: from Edges to Instances with MultiCut
Alexander Kirillov, Evgeny Levinkov, Bjoern Andres +2
This work addresses the task of instance-aware semantic segmentation. Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different…
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
Evgeny Levinkov, Jonas Uhrig, Siyu Tang +7
We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical a…
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother +1
We consider the problem of jointly inferring the M-best diverse labelings for a binary (high-order) submodular energy of a graphical model. Recently, it was shown that this problem…
Joint Training of Generic CNN-CRF Models with Stochastic Optimization
Alexander Kirillov, Dmitrij Schlesinger, Shuai Zheng +3
We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Rand…