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 1931–1940 of 2262 papers

Title	Date	Tasks	Status	Hype
Point Cloud Instance Segmentation using Probabilistic Embeddings	Nov 30, 2019	3D Instance SegmentationClustering	—Unverified	0
Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response	Nov 29, 2019	Cell SegmentationCultural Vocal Bursts Intensity Prediction	CodeCode Available	1
Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards	Nov 28, 2019	Computational EfficiencyInstance Segmentation	—Unverified	0
PanDA: Panoptic Data Augmentation	Nov 27, 2019	Data AugmentationInstance Segmentation	—Unverified	0
Deeply Shape-guided Cascade for Instance Segmentation	Nov 25, 2019	Instance SegmentationRegion Proposal	CodeCode Available	0
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation	Nov 22, 2019	DecoderInstance Segmentation	CodeCode Available	0
Rethinking Normalization and Elimination Singularity in Neural Networks	Nov 21, 2019	image-classificationImage Classification	CodeCode Available	0
All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting	Nov 21, 2019	AllInstance Segmentation	—Unverified	0
Object-Guided Instance Segmentation for Biological Images	Nov 20, 2019	ClusteringInstance Segmentation	—Unverified	0
SOGNet: Scene Overlap Graph Network for Panoptic Segmentation	Nov 18, 2019	Instance SegmentationPanoptic Segmentation	CodeCode Available	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	GLEE-Pro	mask AP	54.2	—	Unverified
9	ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)	mask AP	54.2	—	Unverified
10	SwinV2-G (HTC++)	mask AP	53.7	—	Unverified