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.
3D INSTANCE SEGMENTATION HUMAN PART SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING MULTI-PERSON POSE ESTIMATION MULTI-TISSUE NUCLEUS SEGMENTATION NUCLEAR SEGMENTATION PANOPTIC SEGMENTATION REAL-TIME OBJECT DETECTION
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.
#7 best model for Keypoint Detection on COCO (Validation AP metric)
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.
#17 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
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.
#6 best model for Instance Segmentation on COCO test-dev
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.
#8 best model for Instance Segmentation on COCO test-dev