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 501–510 of 2262 papers

Title	Date	Tasks	Status	Hype	Score
D2Det: Towards High Quality Object Detection and Instance Segmentation	Jun 1, 2020	Instance SegmentationObject	CodeCode Available	1	5
DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting	Dec 2, 2021	Image-text matchingInstance Segmentation	CodeCode Available	1	5
Delving Deeper into Anti-aliasing in ConvNets	Aug 21, 2020	Instance SegmentationSegmentation	CodeCode Available	1	5
Dense Contrastive Learning for Self-Supervised Visual Pre-Training	Nov 18, 2020	Contrastive Learningimage-classification	CodeCode Available	1	5
Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks	Nov 22, 2021	Instance SegmentationObject	CodeCode Available	1	5
Instance Segmentation in the Dark	Apr 27, 2023	Instance SegmentationObject Detection	CodeCode Available	1	5
Instance Segmentation of Dense and Overlapping Objects via Layering	Oct 7, 2022	Instance SegmentationObject	CodeCode Available	1	5
Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints	Apr 21, 2020	Cell SegmentationInstance Segmentation	CodeCode Available	1	5
Instance Segmentation under Occlusions via Location-aware Copy-Paste Data Augmentation	Oct 27, 2023	Data AugmentationInstance Segmentation	CodeCode Available	1	5
Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets	Jul 28, 2024	image-classificationImage Classification	CodeCode Available	1	5

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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