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This paper explores the use of extreme points in an object (left-most, right-most, top, bottom pixels) as input to obtain precise object segmentation for images and videos.
We propose f-BRS (feature backpropagating refinement scheme) that solves an optimization problem with respect to auxiliary variables instead of the network inputs, and requires running forward and backward pass just for a small part of a network.
Ranked #2 on Interactive Segmentation on SBD
We propose a novel interactive architecture and a novel training scheme that are both tailored to better exploit the user workflow.
Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for correcting errors.
This paper explores how to harvest precise object segmentation masks while minimizing the human interaction cost.
Ranked #1 on Interactive Segmentation on COCO
We find that the models trained on a combination of COCO and LVIS with diverse and high-quality annotations show performance superior to all existing models.
Ranked #1 on Interactive Segmentation on GrabCut
We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy.
Starting from an initial output based on the image only, our network then interactively refines this segmentation map using a concatenation of the image and user annotations.