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 2001–2010 of 2262 papers

Title	Date	Tasks	Status
Self-supervised learning for autonomous vehicles perception: A conciliation between analytical and learning methods	Oct 3, 2019	Autonomous DrivingAutonomous Vehicles	—Unverified
LIP: Learning Instance Propagation for Video Object Segmentation	Sep 30, 2019	Data AugmentationInstance Segmentation	—Unverified
Multi-view PointNet for 3D Scene Understanding	Sep 30, 2019	3D Instance Segmentation3D Semantic Segmentation	—Unverified
End-to-End Deep Convolutional Active Contours for Image Segmentation	Sep 29, 2019	Image SegmentationInstance Segmentation	—Unverified
Salient Instance Segmentation via Subitizing and Clustering	Sep 29, 2019	ClusteringInstance Segmentation	—Unverified
Rethinking Task and Metrics of Instance Segmentation on 3D Point Clouds	Sep 27, 2019	Instance SegmentationSemantic Segmentation	—Unverified
Geomorphological Analysis Using Unpiloted Aircraft Systems, Structure from Motion, and Deep Learning	Sep 27, 2019	Instance SegmentationMorphological Analysis	—Unverified
Multi-Task Learning via Scale Aware Feature Pyramid Networks and Effective Joint Head	Sep 25, 2019	Instance SegmentationMulti-Task Learning	—Unverified
A closer look at network resolution for efficient network design	Sep 25, 2019	Instance Segmentationobject-detection	—Unverified
Rescan: Inductive Instance Segmentation for Indoor RGBD Scans	Sep 25, 2019	Instance SegmentationSegmentation	—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