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 2251–2262 of 2262 papers

Title	Date	Tasks	Status
Bridging Category-level and Instance-level Semantic Image Segmentation	May 23, 2016	Image SegmentationInstance Segmentation	—Unverified
Virtual Worlds as Proxy for Multi-Object Tracking Analysis	May 20, 2016	Instance SegmentationMulti-Object Tracking	—Unverified
Amodal Instance Segmentation	Apr 27, 2016	Amodal Instance SegmentationInstance Segmentation	—Unverified
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling	Apr 18, 2016	Instance SegmentationSemantic Segmentation	—Unverified
A MultiPath Network for Object Detection	Apr 7, 2016	Instance SegmentationObject	CodeCode Available
Simple Does It: Weakly Supervised Instance and Semantic Segmentation	Mar 24, 2016	Instance SegmentationSegmentation	—Unverified
Iterative Instance Segmentation	Nov 26, 2015	Instance SegmentationPrediction	—Unverified
Recurrent Instance Segmentation	Nov 25, 2015	Instance SegmentationOcclusion Handling	—Unverified
Learning to decompose for object detection and instance segmentation	Nov 19, 2015	Instance SegmentationObject	—Unverified
Monocular Object Instance Segmentation and Depth Ordering with CNNs	May 12, 2015	Instance SegmentationObject	—Unverified
Learning Rich Features from RGB-D Images for Object Detection and Segmentation	Jul 22, 2014	Instance SegmentationObject	CodeCode Available
An Exemplar-based CRF for Multi-instance Object Segmentation	Jun 1, 2014	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	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