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 101–110 of 2262 papers

Title	Date	Tasks	Status	Hype
Mask-Free Video Instance Segmentation	Mar 28, 2023	Instance SegmentationOptical Flow Estimation	CodeCode Available	2
FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation	Mar 15, 2023	DecoderInstance Segmentation	CodeCode Available	2
CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attention	Mar 13, 2023	image-classificationImage Classification	CodeCode Available	2
DiffusionInst: Diffusion Model for Instance Segmentation	Dec 6, 2022	DenoisingInstance Segmentation	CodeCode Available	2
Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution	Dec 3, 2022	Box-supervised Instance SegmentationDecoder	CodeCode Available	2
PLA: Language-Driven Open-Vocabulary 3D Scene Understanding	Nov 29, 2022	3D Open-Vocabulary Instance SegmentationContrastive Learning	CodeCode Available	2
MogaNet: Multi-order Gated Aggregation Network	Nov 7, 2022	3D Human Pose EstimationImage Classification	CodeCode Available	2
What the DAAM: Interpreting Stable Diffusion Using Cross Attention	Oct 10, 2022	DenoisingDescriptive	CodeCode Available	2
Mask3D: Mask Transformer for 3D Semantic Instance Segmentation	Oct 6, 2022	3D Instance Segmentation3D Semantic Instance Segmentation	CodeCode Available	2
Dilated Neighborhood Attention Transformer	Sep 29, 2022	Image ClassificationInstance Segmentation	CodeCode Available	2

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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