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 801–810 of 2262 papers

Title	Date	Tasks	Status	Hype
Instance Segmentation under Occlusions via Location-aware Copy-Paste Data Augmentation	Oct 27, 2023	Data AugmentationInstance Segmentation	CodeCode Available	1
FsaNet: Frequency Self-attention for Semantic Segmentation	Nov 28, 2022	Instance SegmentationSemantic Segmentation	CodeCode Available	1
Clustering Plotted Data by Image Segmentation	Oct 6, 2021	ClusteringImage Segmentation	CodeCode Available	1
StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images	Mar 14, 2024	Computational EfficiencyInstance Segmentation	CodeCode Available	1
Fully Automated Scan-to-BIM Via Point Cloud Instance Segmentation	Sep 11, 2023	3D Instance Segmentation3D Semantic Segmentation	CodeCode Available	1
LeafMask: Towards Greater Accuracy on Leaf Segmentation	Aug 8, 2021	Instance SegmentationPlant Phenotyping	CodeCode Available	1
Fully Sparse Fusion for 3D Object Detection	Apr 24, 2023	3D Instance Segmentation3D Object Detection	CodeCode Available	1
LVIS: A Dataset for Large Vocabulary Instance Segmentation	Aug 8, 2019	Instance SegmentationObject	CodeCode Available	1
GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation	Sep 21, 2024	Instance SegmentationSegmentation	CodeCode Available	1
You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding	Mar 26, 2023	3D Instance SegmentationInstance Segmentation	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