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 2171–2180 of 2262 papers

Title	Date	Tasks	Status
Learning Instance Segmentation by Interaction	Jun 21, 2018	Instance SegmentationSegmentation	CodeCode Available
Instance Search via Instance Level Segmentation and Feature Representation	Jun 10, 2018	Instance SearchInstance Segmentation	—Unverified
Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks	Jun 6, 2018	Instance SegmentationSegmentation	—Unverified
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images	Jun 5, 2018	Colorectal Gland Segmentation:Decision Making	—Unverified
Bayesian Semantic Instance Segmentation in Open Set World	Jun 4, 2018	Instance SegmentationObject	—Unverified
TernausNetV2: Fully Convolutional Network for Instance Segmentation	Jun 3, 2018	DecoderInstance Segmentation	CodeCode Available
Free Supervision From Video Games	Jun 1, 2018	Depth EstimationInstance Segmentation	—Unverified
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines	Jun 1, 2018	Instance SegmentationPose Estimation	—Unverified
Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network	May 26, 2018	Instance SegmentationMulti-Task Learning	—Unverified
Adapting Mask-RCNN for Automatic Nucleus Segmentation	May 1, 2018	Instance SegmentationObject	—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