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 2091–2100 of 2262 papers

Title	Date	Tasks	Status	Hype
Primitive-based 3D Building Modeling, Sensor Simulation, and Estimation	Jan 16, 2019	Instance SegmentationSemantic Segmentation	—Unverified	0
UPSNet: A Unified Panoptic Segmentation Network	Jan 12, 2019	Instance SegmentationPanoptic Segmentation	CodeCode Available	0
Panoptic Feature Pyramid Networks	Jan 8, 2019	Instance SegmentationPanoptic Segmentation	CodeCode Available	4
Instance Segmentation of Fibers from Low Resolution CT Scans via 3D Deep Embedding Learning	Jan 4, 2019	3D Instance SegmentationClustering	—Unverified	0
Slimmable Neural Networks	Dec 21, 2018	Instance SegmentationKeypoint Detection	CodeCode Available	1
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation	Dec 19, 2018	Instance SegmentationObject	—Unverified	0
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM	Dec 19, 2018	CPUInstance Segmentation	CodeCode Available	1
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans	Dec 17, 2018	3D Instance Segmentation3D Object Detection	CodeCode Available	0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation	Dec 13, 2018	Instance SegmentationObject	—Unverified	0
Weakly Supervised Instance Segmentation Using Hybrid Network	Dec 12, 2018	Image SegmentationInstance 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