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 1461–1470 of 2262 papers

Title	Date	Tasks	Status
Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images	Nov 22, 2023	Instance SegmentationPrognosis	—Unverified
Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation	Jun 28, 2018	3D Instance SegmentationDeep Learning	—Unverified
Deep Learning Based Video System for Accurate and Real-Time Parking Measurement	Feb 20, 2019	Instance SegmentationSemantic Segmentation	—Unverified
Deep Learning Techniques for Video Instance Segmentation: A Survey	Oct 19, 2023	Action RecognitionDeep Learning	—Unverified
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation	Jun 15, 2022	Domain AdaptationInstance Segmentation	—Unverified
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images	Jun 15, 2022	Instance SegmentationMedical Image Analysis	—Unverified
Deep Polarization Cues for Transparent Object Segmentation	Jun 1, 2020	Instance SegmentationObject	—Unverified
Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge	Feb 14, 2024	Fracture detectionInstance Segmentation	—Unverified
Deep Semantic Instance Segmentation of Tree-like Structures Using Synthetic Data	Nov 8, 2018	Instance SegmentationSegmentation	—Unverified
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation	Jul 3, 2018	Instance SegmentationSemantic Segmentation	—Unverified

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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