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 2161–2170 of 2262 papers

Title	Date	Tasks	Status
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation	Jul 16, 2018	3D Object DetectionAutonomous Driving	—Unverified
Non-local RoIs for Instance Segmentation	Jul 14, 2018	Instance SegmentationSegmentation	—Unverified
Dynamic Multimodal Instance Segmentation guided by natural language queries	Jul 6, 2018	Instance SegmentationNatural Language Queries	CodeCode Available
Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products	Jul 5, 2018	Instance SegmentationObject	—Unverified
Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network	Jul 5, 2018	Instance SegmentationSegmentation	—Unverified
Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation	Jul 4, 2018	Instance SegmentationRobot Navigation	—Unverified
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation	Jul 3, 2018	Instance SegmentationSemantic Segmentation	—Unverified
A Dataset for Lane Instance Segmentation in Urban Environments	Jul 3, 2018	Autonomous VehiclesInstance Segmentation	—Unverified
Active Testing: An Efficient and Robust Framework for Estimating Accuracy	Jul 2, 2018	Instance SegmentationMulti-Label Classification	—Unverified
Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation	Jun 28, 2018	3D Instance SegmentationDeep Learning	—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	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