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 201–210 of 2262 papers

Title	Date	Tasks	Status	Hype
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	0
Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks	Oct 24, 2024	Instance SegmentationSegmentation	—Unverified	0
YOLOv11: An Overview of the Key Architectural Enhancements	Oct 23, 2024	Computational EfficiencyInstance Segmentation	CodeCode Available	0
NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation	Oct 22, 2024	Data AugmentationInstance Segmentation	—Unverified	0
DI-MaskDINO: A Joint Object Detection and Instance Segmentation Model	Oct 22, 2024	DecoderInstance Segmentation	CodeCode Available	2
EPContrast: Effective Point-level Contrastive Learning for Large-scale Point Cloud Understanding	Oct 22, 2024	Contrastive LearningInductive Bias	—Unverified	0
PlaneSAM: Multimodal Plane Instance Segmentation Using the Segment Anything Model	Oct 21, 2024	Instance SegmentationPlane Instance Segmentation	—Unverified	0
Integrated Image-Text Based on Semi-supervised Learning for Small Sample Instance Segmentation	Oct 21, 2024	Instance SegmentationMeta-Learning	—Unverified	0
Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing	Oct 20, 2024	Image SegmentationInstance Segmentation	CodeCode Available	0
Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images	Oct 18, 2024	Cell SegmentationElectron Microscopy Image Segmentation	CodeCode Available	0

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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