Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.
( Image credit: Weakly Supervised Panoptic Segmentation )
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Ranked #1 on Real-Time Object Detection on COCO minival (MAP metric)
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
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.
Ranked #19 on 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.
Ranked #4 on Panoptic Segmentation on KITTI Panoptic Segmentation
In this paper, we explore this mechanism in the backbone design for object detection.
Ranked #1 on Instance Segmentation on COCO test-dev
In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extract a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone.
Ranked #15 on Instance Segmentation on COCO minival
In existing CNN based detectors, the backbone network is a very important component for basic feature extraction, and the performance of the detectors highly depends on it.
Ranked #3 on Instance Segmentation on COCO test-dev