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 1011–1020 of 2262 papers

Title	Date	Tasks	Status
Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN	Apr 6, 2022	Generative Adversarial NetworkImage Segmentation	—Unverified
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects	Jan 21, 2020	Image GenerationInstance Segmentation	—Unverified
Comparison of UNet, ENet, and BoxENet for Segmentation of Mast Cells in Scans of Histological Slices	Sep 15, 2019	Instance SegmentationSemantic Segmentation	—Unverified
Instance Segmentation with Cross-Modal Consistency	Oct 14, 2022	Autonomous DrivingContrastive Learning	—Unverified
Instance Segmentation With Mask-Supervised Polygonal Boundary Transformers	Jan 1, 2022	Instance SegmentationSegmentation	—Unverified
Instance Segmentation with Point Supervision	Jun 14, 2019	Instance SegmentationObject	—Unverified
Instance segmentation with the number of clusters incorporated in embedding learning	Mar 27, 2021	ClusteringImage Segmentation	—Unverified
Instance Segmentation XXL-CT Challenge of a Historic Airplane	Feb 5, 2024	Image SegmentationInstance Segmentation	—Unverified
Instance-Specific Feature Propagation for Referring Segmentation	Apr 26, 2022	Instance SegmentationSegmentation	—Unverified
Competitive Learning for Achieving Content-specific Filters in Video Coding for Machines	Jun 18, 2024	Instance Segmentationobject-detection	—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