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 2111–2120 of 2262 papers

Title	Date	Tasks	Status
Primitive-based 3D Building Modeling, Sensor Simulation, and Estimation	Jan 16, 2019	Instance SegmentationSemantic Segmentation	—Unverified
UPSNet: A Unified Panoptic Segmentation Network	Jan 12, 2019	Instance SegmentationPanoptic Segmentation	CodeCode Available
Instance Segmentation of Fibers from Low Resolution CT Scans via 3D Deep Embedding Learning	Jan 4, 2019	3D Instance SegmentationClustering	—Unverified
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation	Dec 19, 2018	Instance SegmentationObject	—Unverified
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans	Dec 17, 2018	3D Instance Segmentation3D Object Detection	CodeCode Available
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation	Dec 13, 2018	Instance SegmentationObject	—Unverified
Weakly Supervised Instance Segmentation Using Hybrid Network	Dec 12, 2018	Image SegmentationInstance Segmentation	—Unverified
Scale-aware multi-level guidance for interactive instance segmentation	Dec 7, 2018	Instance SegmentationInteractive Segmentation	—Unverified
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding	Dec 6, 2018	3D Instance Segmentation3D Semantic Segmentation	CodeCode Available
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics	Dec 4, 2018	ClusteringGeneral Classification	—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