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 1891–1900 of 2262 papers

Title	Date	Tasks	Status
Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers	Jul 6, 2020	ClusteringInstance Segmentation	—Unverified
Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition	Jun 29, 2020	Anomaly DetectionClustering	—Unverified
A Survey on Instance Segmentation: State of the art	Jun 28, 2020	Instance SegmentationObject	—Unverified
SASO: Joint 3D Semantic-Instance Segmentation via Multi-scale Semantic Association and Salient Point Clustering Optimization	Jun 25, 2020	3D Instance Segmentation3D Semantic Instance Segmentation	—Unverified
NINEPINS: Nuclei Instance Segmentation with Point Annotations	Jun 24, 2020	Instance SegmentationPseudo Label	—Unverified
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB	Jun 17, 2020	Autonomous VehiclesInstance Segmentation	—Unverified
Geometry-Aware Instance Segmentation with Disparity Maps	Jun 14, 2020	Instance SegmentationSemantic Segmentation	CodeCode Available
Instance segmentation of buildings using keypoints	Jun 6, 2020	Instance SegmentationSegmentation	—Unverified
Density-Based Clustering for 3D Object Detection in Point Clouds	Jun 1, 2020	3D Object DetectionClustering	—Unverified
Video Instance Segmentation Tracking With a Modified VAE Architecture	Jun 1, 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