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 2221–2230 of 2262 papers

Title	Date	Tasks	Status
Leveraging multiple datasets for deep leaf counting	Sep 5, 2017	Instance SegmentationSegmentation	—Unverified
Fast Scene Understanding for Autonomous Driving	Aug 8, 2017	Autonomous DrivingDecoder	CodeCode Available
Semantic Instance Segmentation with a Discriminative Loss Function	Aug 8, 2017	Instance SegmentationLane Detection	CodeCode Available
Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes	Aug 4, 2017	Instance SegmentationObject	—Unverified
Spatio-temporal Human Action Localisation and Instance Segmentation in Temporally Untrimmed Videos	Jul 22, 2017	Action RecognitionInstance Segmentation	—Unverified
Deep GrabCut for Object Selection	Jul 2, 2017	DecoderInstance Segmentation	—Unverified
Indoor Scene Parsing With Instance Segmentation, Semantic Labeling and Support Relationship Inference	Jul 1, 2017	Instance SegmentationScene Parsing	—Unverified
Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects	Jun 20, 2017	Instance SegmentationObject	CodeCode Available
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks	Jun 7, 2017	Bayesian InferenceInstance Segmentation	—Unverified
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition	Apr 24, 2017	Instance SegmentationObject	—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	GLEE-Pro	mask AP	54.2	—	Unverified
9	ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)	mask AP	54.2	—	Unverified
10	SwinV2-G (HTC++)	mask AP	53.7	—	Unverified