Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.
( Image credit: Weakly Supervised Panoptic Segmentation )
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Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
SOTA for Instance Segmentation on Cityscapes test (using extra training data)
In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation.
#2 best model for Instance Segmentation on COCO minival
In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.
SOTA for Instance Segmentation on COCO test-dev (using extra training data)
The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects.
#15 best model for Object Detection on COCO test-dev
To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors.
#10 best model for Instance Segmentation on COCO test-dev
In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks.
#2 best model for Panoptic Segmentation on COCO panoptic