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 971–980 of 2262 papers

Title	Date	Tasks	Status
Tree level change detection over Ahmedabad city using very high resolution satellite images and Deep Learning	Nov 4, 2024	Change DetectionInstance Segmentation	—Unverified
MV-Adapter: Enhancing Underwater Instance Segmentation via Adaptive Channel Attention	Nov 1, 2024	Instance SegmentationSegmentation	—Unverified
Comparing YOLOv11 and YOLOv8 for instance segmentation of occluded and non-occluded immature green fruits in complex orchard environment	Oct 24, 2024	Instance SegmentationSegmentation	—Unverified
Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks	Oct 24, 2024	Instance SegmentationSegmentation	—Unverified
YOLOv11: An Overview of the Key Architectural Enhancements	Oct 23, 2024	Computational EfficiencyInstance Segmentation	CodeCode Available
NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation	Oct 22, 2024	Data AugmentationInstance Segmentation	—Unverified
EPContrast: Effective Point-level Contrastive Learning for Large-scale Point Cloud Understanding	Oct 22, 2024	Contrastive LearningInductive Bias	—Unverified
PlaneSAM: Multimodal Plane Instance Segmentation Using the Segment Anything Model	Oct 21, 2024	Instance SegmentationPlane Instance Segmentation	—Unverified
Integrated Image-Text Based on Semi-supervised Learning for Small Sample Instance Segmentation	Oct 21, 2024	Instance SegmentationMeta-Learning	—Unverified
Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing	Oct 20, 2024	Image SegmentationInstance Segmentation	CodeCode Available

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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