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 791–800 of 2262 papers

Title	Date	Tasks	Status	Hype
Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art	Apr 25, 2023	ArticlesInstance Segmentation	—Unverified	0
AutoFocusFormer: Image Segmentation off the Grid	Apr 24, 2023	Image SegmentationInstance Segmentation	CodeCode Available	1
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer	Apr 24, 2023	Data AugmentationImage Augmentation	CodeCode Available	1
Fully Sparse Fusion for 3D Object Detection	Apr 24, 2023	3D Instance Segmentation3D Object Detection	CodeCode Available	1
Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models	Apr 20, 2023	Instance SegmentationSegmentation	CodeCode Available	1
Ensembling Instance and Semantic Segmentation for Panoptic Segmentation	Apr 20, 2023	Instance SegmentationPanoptic Segmentation	—Unverified	0
Baybayin Character Instance Detection	Apr 19, 2023	image-classificationImage Classification	—Unverified	0
Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation	Apr 18, 2023	Instance SegmentationObject	—Unverified	0
UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite	Apr 18, 2023	BenchmarkingInstance Segmentation	—Unverified	0
Self-Supervised Learning from Non-Object Centric Images with a Geometric Transformation Sensitive Architecture	Apr 17, 2023	image-classificationImage Classification	CodeCode Available	0

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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