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 1151–1160 of 2262 papers

Title	Date	Tasks	Status	Score
Training a universal instance segmentation network for live cell images of various cell types and imaging modalities	Jul 28, 2022	Cell TrackingInstance Segmentation	CodeCode Available	5
End-to-end video instance segmentation via spatial-temporal graph neural networks	Mar 7, 2022	Graph Neural NetworkInstance Segmentation	CodeCode Available	5
MWSIS: Multimodal Weakly Supervised Instance Segmentation with 2D Box Annotations for Autonomous Driving	Dec 12, 2023	3D Instance SegmentationAutonomous Driving	CodeCode Available	5
Training-Free Dataset Pruning for Instance Segmentation	Mar 2, 2025	Instance SegmentationSegmentation	CodeCode Available	5
CCNet: Criss-Cross Attention for Semantic Segmentation	Nov 28, 2018	Computational EfficiencyGPU	CodeCode Available	5
Vision Transformer with Sparse Scan Prior	May 22, 2024	Instance Segmentationobject-detection	CodeCode Available	5
SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation	Jul 11, 2024	Instance SegmentationSegmentation	CodeCode Available	5
Instance Segmentation of Microscopic Foraminifera	May 15, 2021	Instance SegmentationNovel Object Detection	CodeCode Available	5
Slum Segmentation and Change Detection : A Deep Learning Approach	Nov 19, 2018	Change DetectionDeep Learning	CodeCode Available	5
DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images	Apr 3, 2021	Domain AdaptationInstance Segmentation	CodeCode Available	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	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