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 1921–1930 of 2262 papers

Title	Date	Tasks	Status
Convolutional Neural Networks based automated segmentation and labelling of the lumbar spine X-ray	Apr 4, 2020	Instance SegmentationSegmentation	—Unverified
Pixel Consensus Voting for Panoptic Segmentation	Apr 4, 2020	Instance SegmentationPanoptic Segmentation	—Unverified
FGN: Fully Guided Network for Few-Shot Instance Segmentation	Mar 31, 2020	Few-shot Instance SegmentationFew-Shot Learning	—Unverified
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation	Mar 31, 2020	Instance SegmentationOcclusion Handling	CodeCode Available
EOLO: Embedded Object Segmentation only Look Once	Mar 31, 2020	Distance regressionGPU	CodeCode Available
Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting	Mar 30, 2020	Instance SegmentationRobotic Grasping	—Unverified
Spatial Attention Pyramid Network for Unsupervised Domain Adaptation	Mar 29, 2020	Domain AdaptationInstance Segmentation	—Unverified
Mask Encoding for Single Shot Instance Segmentation	Mar 26, 2020	Instance SegmentationSegmentation	—Unverified
Robust Medical Instrument Segmentation Challenge 2019	Mar 23, 2020	BenchmarkingInstance Segmentation	—Unverified
Neural Mesh Refiner for 6-DoF Pose Estimation	Mar 17, 2020	Autonomous DrivingInstance Segmentation	—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