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 1361–1370 of 2262 papers

Title	Date	Tasks	Status
Language-Based Depth Hints for Monocular Depth Estimation	Mar 22, 2024	Depth EstimationInstance Segmentation	—Unverified
Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation	Apr 4, 2024	Autonomous DrivingDomain Adaptation	—Unverified
Large-scale interactive object segmentation with human annotators	Mar 26, 2019	Instance SegmentationInteractive Segmentation	—Unverified
Layout Aware Inpainting for Automated Furniture Removal in Indoor Scenes	Oct 27, 2022	Instance SegmentationSemantic Segmentation	—Unverified
LDD: A Dataset for Grape Diseases Object Detection and Instance Segmentation	Jun 21, 2022	Instance SegmentationObject	—Unverified
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment	Jun 17, 2025	Autonomous DrivingInstance Segmentation	—Unverified
Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation	Jan 6, 2020	3D Instance SegmentationInstance Segmentation	—Unverified
Learning a unified label space	Jan 1, 2021	Instance SegmentationObject	—Unverified
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation	Jul 9, 2021	Autonomous DrivingDomain Adaptation	—Unverified
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation	Sep 28, 2020	Instance SegmentationPanoptic Segmentation	—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