Instance Segmentation

Instance Segmentation is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object. The goal of instance segmentation is to produce a pixel-wise segmentation map of the image, where each pixel is assigned to a specific object instance.

Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers, CVPR'21

Papers

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Showing 1941–1950 of 2262 papers

Title	Date	Tasks	Status
Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation	Feb 6, 2020	Distance regressionImage Augmentation	—Unverified
Weakly Supervised Instance Segmentation by Deep Community Learning	Jan 30, 2020	Image-level Supervised Instance SegmentationInstance Segmentation	—Unverified
Virtual KITTI 2	Jan 29, 2020	Autonomous DrivingInstance Segmentation	—Unverified
Accurately identifying vertebral levels in large datasets	Jan 28, 2020	Instance SegmentationSegmentation	—Unverified
SDOD:Real-time Segmenting and Detecting 3D Object by Depth	Jan 26, 2020	Autonomous DrivingInstance Segmentation	—Unverified
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects	Jan 21, 2020	Image GenerationInstance Segmentation	—Unverified
Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter	Jan 21, 2020	Instance SegmentationSegmentation	—Unverified
GraphBGS: Background Subtraction via Recovery of Graph Signals	Jan 17, 2020	Change Detectiongraph construction	—Unverified
Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey	Jan 13, 2020	Image SegmentationInstance Segmentation	—Unverified
SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark	Jan 10, 2020	Instance Segmentationobject-detection	—Unverified

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All datasets COCO test-dev COCO minival LVIS v1.0 val Cityscapes val ADE20K val ARMBench Occluded COCO OoDIS Separated COCO TBBR BDD100K val COCO val (panoptic labels)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	InternImage-H	AP50	80.8	—	Unverified
2	ResNeSt-200 (multi-scale)	AP50	70.2	—	Unverified
3	CenterMask + VoVNetV2-99 (multi-scale)	AP50	66.2	—	Unverified
4	CenterMask + VoVNetV2-57 (single-scale)	AP50	60.8	—	Unverified
5	Co-DETR	mask AP	57.1	—	Unverified
6	CBNetV2 (EVA02, single-scale)	mask AP	56.1	—	Unverified
7	ISDA (ResNet-50)	APL	55.7	—	Unverified
8	EVA	mask AP	55.5	—	Unverified
9	FD-SwinV2-G	mask AP	55.4	—	Unverified
10	Mask Frozen-DETR	mask AP	55.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	InternImage-B	GFLOPs	501	—	Unverified
2	Co-DETR	mask AP	56.6	—	Unverified
3	ViT-CoMer-L (Mask RCNN, DINOv2)	mask AP	55.9	—	Unverified
4	InternImage-H	mask AP	55.4	—	Unverified
5	EVA	mask AP	55	—	Unverified
6	Mask Frozen-DETR	mask AP	54.9	—	Unverified
7	MasK DINO (SwinL, multi-scale)	mask AP	54.5	—	Unverified
8	ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)	mask AP	54.2	—	Unverified
9	GLEE-Pro	mask AP	54.2	—	Unverified
10	SwinV2-G (HTC++)	mask AP	53.7	—	Unverified