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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1991–2000 of 2262 papers

Title	Date	Tasks	Status	Hype
Instance Scale Normalization for image understanding	Aug 20, 2019	Instance SegmentationObject	—Unverified	0
IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation	Aug 19, 2019	Cell SegmentationInstance Segmentation	CodeCode Available	0
Explicit Shape Encoding for Real-Time Instance Segmentation	Aug 12, 2019	Instance SegmentationObject	CodeCode Available	0
Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion	Aug 10, 2019	Instance SegmentationNuclear Segmentation	CodeCode Available	1
LVIS: A Dataset for Large Vocabulary Instance Segmentation	Aug 8, 2019	Instance SegmentationObject	CodeCode Available	1
Attentive Normalization	Aug 4, 2019	Image ClassificationInstance Segmentation	CodeCode Available	0
The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation	Jul 30, 2019	Instance SegmentationObject	—Unverified	0
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation	Jul 26, 2019	Breast Cancer DetectionInstance Segmentation	—Unverified	0
Grape detection, segmentation and tracking using deep neural networks and three-dimensional association	Jul 26, 2019	Instance SegmentationSegmentation	CodeCode Available	0
Object as Distribution	Jul 25, 2019	Autonomous DrivingInstance Segmentation	—Unverified	0

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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