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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 781–790 of 2262 papers

Title	Date	Tasks	Status	Hype
FoodSAM: Any Food Segmentation	Aug 11, 2023	Image SegmentationInstance Segmentation	CodeCode Available	1
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline	Dec 11, 2020	DiagnosticGeneral Classification	CodeCode Available	1
Instance Semantic Segmentation Benefits from Generative Adversarial Networks	Oct 26, 2020	Autonomous DrivingInstance Segmentation	CodeCode Available	1
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation	Aug 13, 2020	ClassificationFew-Shot Object Detection	CodeCode Available	1
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak Supervision	Jan 1, 2023	Continual LearningIncremental Learning	CodeCode Available	1
SEMv2: Table Separation Line Detection Based on Instance Segmentation	Mar 8, 2023	Instance SegmentationLine Detection	CodeCode Available	1
HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification	Feb 12, 2025	Cell SegmentationImage Generation	CodeCode Available	1
FourierNet: Compact mask representation for instance segmentation using differentiable shape decoders	Feb 7, 2020	DecoderInstance Segmentation	CodeCode Available	1
SGIFormer: Semantic-guided and Geometric-enhanced Interleaving Transformer for 3D Instance Segmentation	Jul 16, 2024	3D Instance SegmentationDecoder	CodeCode Available	1
Improving Mask R-CNN for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images	Jul 20, 2021	Instance SegmentationSegmentation	CodeCode Available	1

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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