Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.
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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)
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.
#3 best model for Instance Segmentation on COCO minival
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 this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks.
#4 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.
#9 best model for Object Detection on COCO test-dev
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
We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).
#2 best model for Image-to-Image Translation on ADE20K-Outdoor Labels-to-Photos