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 address the imbalance between foreground and background, various heuristic methods, such as OHEM, Focal Loss, GHM, have been proposed for biased sampling or weighting when training deep object detectors.
We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization.
SOTA for Object Detection on COCO minival