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 2111–2120 of 2262 papers

Title	Date	Tasks	Status
Towards Segmenting Anything That Moves	Feb 11, 2019	Action DetectionInstance Segmentation	CodeCode Available
Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose Encoding	Apr 17, 2024	3D Pose EstimationInstance Segmentation	CodeCode Available
Dynamic Multimodal Instance Segmentation guided by natural language queries	Jul 6, 2018	Instance SegmentationNatural Language Queries	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
ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detection	Jan 6, 2024	3D Instance SegmentationInstance Segmentation	CodeCode Available
Multi-task Planar Reconstruction with Feature Warping Guidance	Nov 25, 2023	3D ReconstructionInstance Segmentation	CodeCode Available
Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes	Jul 22, 2019	Cell SegmentationInstance Segmentation	CodeCode Available
GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation	Dec 12, 2021	BenchmarkingInstance Segmentation	CodeCode Available
SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation	Jan 18, 2024	Instance SegmentationSemantic Segmentation	CodeCode Available
Self-Balanced R-CNN for Instance Segmentation	Apr 25, 2024	Instance Segmentationobject-detection	CodeCode Available

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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