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 1211–1220 of 2262 papers

Title	Date	Tasks	Status
Accelerating the creation of instance segmentation training sets through bounding box annotation	May 23, 2022	Instance SegmentationObject	—Unverified
Accurate and efficient zero-shot 6D pose estimation with frozen foundation models	Jun 11, 2025	6D Pose EstimationInstance Segmentation	—Unverified
Accurately identifying vertebral levels in large datasets	Jan 28, 2020	Instance SegmentationSegmentation	—Unverified
ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception	Apr 27, 2021	Instance Segmentationobject-detection	—Unverified
A Closed-Loop System for Improving Annotation Quality and Efficiency	Oct 15, 2020	Autonomous DrivingInstance Segmentation	—Unverified
A closer look at network resolution for efficient network design	Sep 25, 2019	Instance Segmentationobject-detection	—Unverified
A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation	Jun 18, 2021	Distance regressionInstance Segmentation	—Unverified
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects	Jun 16, 2025	BenchmarkingInstance Segmentation	—Unverified
A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color	Mar 5, 2020	3D Instance SegmentationAutonomous Driving	—Unverified
Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products	Jul 5, 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