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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 671–680 of 2262 papers

Title	Date	Tasks	Status	Hype
Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation	Nov 23, 2020	Instance SegmentationSegmentation	CodeCode Available	1
One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks	Nov 21, 2020	AllInstance Segmentation	CodeCode Available	1
Attention-Based Transformers for Instance Segmentation of Cells in Microstructures	Nov 19, 2020	Cell DetectionCell Segmentation	CodeCode Available	1
DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation	Nov 19, 2020	Instance SegmentationSemantic Segmentation	CodeCode Available	1
Dense Contrastive Learning for Self-Supervised Visual Pre-Training	Nov 18, 2020	Contrastive Learningimage-classification	CodeCode Available	1
Towards Efficient Scene Understanding via Squeeze Reasoning	Nov 6, 2020	Instance Segmentationobject-detection	CodeCode Available	1
Lightweight Model For The Prediction of COVID-19 Through The Detection And Segmentation of Lesions in Chest CT Scans	Nov 4, 2020	COVID-19 DiagnosisCOVID-19 Image Segmentation	CodeCode Available	1
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation	Oct 30, 2020	Instance SegmentationPanoptic Segmentation	CodeCode Available	1
Perception for Autonomous Systems (PAZ)	Oct 27, 2020	2D Object Detection6D Pose Estimation	CodeCode Available	1
Instance Semantic Segmentation Benefits from Generative Adversarial Networks	Oct 26, 2020	Autonomous DrivingInstance 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