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 2211–2220 of 2262 papers

Title	Date	Tasks	Status
Learning Regional Purity for Instance Segmentation on 3D Point Clouds	Nov 3, 2020	3D Instance Segmentation3D Semantic Segmentation	CodeCode Available
Learning Panoptic Segmentation from Instance Contours	Oct 16, 2020	ClusteringInstance Segmentation	CodeCode Available
U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument	Feb 24, 2019	Data AugmentationDecoder	CodeCode Available
Learning Instance Segmentation by Interaction	Jun 21, 2018	Instance SegmentationSegmentation	CodeCode Available
Deep Leaf Segmentation Using Synthetic Data	Jul 28, 2018	Instance SegmentationSegmentation	CodeCode Available
WSESeg: Introducing a Dataset for the Segmentation of Winter Sports Equipment with a Baseline for Interactive Segmentation	Jul 12, 2024	Instance SegmentationInteractive Segmentation	CodeCode Available
Learning Instance Occlusion for Panoptic Segmentation	Jun 13, 2019	Instance SegmentationPanoptic Segmentation	CodeCode Available
SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation	Jul 11, 2024	Instance SegmentationSegmentation	CodeCode Available
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans	Dec 17, 2018	3D Instance Segmentation3D Object Detection	CodeCode Available
Slum Segmentation and Change Detection : A Deep Learning Approach	Nov 19, 2018	Change DetectionDeep Learning	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