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 121–130 of 2262 papers

Title	Date	Tasks	Status	Hype	Score
Segment Anything for Histopathology	Feb 1, 2025	Image SegmentationInstance Segmentation	CodeCode Available	2	5
BlenderProc	Oct 25, 2019	3D Object RecognitionDepth Image Estimation	CodeCode Available	2	5
OBSeg: Accurate and Fast Instance Segmentation Framework Using Segmentation Foundation Models with Oriented Bounding Box Prompts	Jan 16, 2024	Amodal Instance SegmentationInstance Segmentation	CodeCode Available	2	5
Simplifying Object Segmentation with PixelLib Library	Jan 20, 2021	Image ClassificationInstance Segmentation	CodeCode Available	2	5
Scalable SoftGroup for 3D Instance Segmentation on Point Clouds	Sep 17, 2022	3D Instance SegmentationInstance Segmentation	CodeCode Available	2	5
Bottleneck Transformers for Visual Recognition	Jan 27, 2021	image-classificationImage Classification	CodeCode Available	2	5
Deep Snake for Real-Time Instance Segmentation	Jan 6, 2020	GPUInstance Segmentation	CodeCode Available	2	5
DaViT: Dual Attention Vision Transformers	Apr 7, 2022	Computational EfficiencyImage Classification	CodeCode Available	2	5
Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery	Apr 3, 2025	Field Boundary DelineationInstance Segmentation	CodeCode Available	2	5
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution	Jun 3, 2020	Instance SegmentationObject	CodeCode Available	2	5

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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	GLEE-Pro	mask AP	54.2	—	Unverified
9	ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)	mask AP	54.2	—	Unverified
10	SwinV2-G (HTC++)	mask AP	53.7	—	Unverified