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 2141–2150 of 2262 papers

Title	Date	Tasks	Status
BshapeNet: Object Detection and Instance Segmentation with Bounding Shape Masks	Oct 15, 2018	Instance SegmentationObject	—Unverified
Artistic Instance-Aware Image Filtering by Convolutional Neural Networks	Sep 22, 2018	Instance Segmentationobject-detection	—Unverified
Faster Training of Mask R-CNN by Focusing on Instance Boundaries	Sep 19, 2018	Edge DetectionInstance Segmentation	CodeCode Available
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data	Sep 16, 2018	ClusteringDataset Generation	CodeCode Available
Robust Adversarial Perturbation on Deep Proposal-based Models	Sep 16, 2018	Instance SegmentationRegion Proposal	—Unverified
Geometric Image Synthesis	Sep 12, 2018	Image GenerationInstance Segmentation	—Unverified
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information	Sep 9, 2018	ClusteringDepth Estimation	—Unverified
On the Importance of Visual Context for Data Augmentation in Scene Understanding	Sep 6, 2018	Data AugmentationInstance Segmentation	—Unverified
Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network	Sep 6, 2018	Instance SegmentationPanoptic Segmentation	—Unverified
WildDash - Creating Hazard-Aware Benchmarks	Sep 1, 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