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 931–940 of 2262 papers

Title	Date	Tasks	Status	Hype
Tracking Passengers and Baggage Items using Multiple Overhead Cameras at Security Checkpoints	Dec 31, 2022	Data AugmentationInstance Segmentation	CodeCode Available	0
PanDepth: Joint Panoptic Segmentation and Depth Completion	Dec 29, 2022	Autonomous DrivingDepth Completion	CodeCode Available	1
CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentation	Dec 28, 2022	Domain AdaptationInstance Segmentation	—Unverified	0
Brain Cancer Segmentation Using YOLOv5 Deep Neural Network	Dec 27, 2022	GPUInstance Segmentation	—Unverified	0
PMODE: Prototypical Mask based Object Dimension Estimation	Dec 26, 2022	Camera CalibrationInstance Segmentation	—Unverified	0
A Close Look at Spatial Modeling: From Attention to Convolution	Dec 23, 2022	Instance Segmentationobject-detection	CodeCode Available	1
Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology	Dec 23, 2022	Contrastive LearningInstance Segmentation	CodeCode Available	0
SupeRGB-D: Zero-shot Instance Segmentation in Cluttered Indoor Environments	Dec 22, 2022	Instance SegmentationObject	CodeCode Available	1
Generalized Decoding for Pixel, Image, and Language	Dec 21, 2022	DecoderImage Segmentation	CodeCode Available	3
Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework	Dec 20, 2022	Instance SegmentationSegmentation	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