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 1481–1490 of 2262 papers

Title	Date	Tasks	Status
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation	Dec 13, 2018	Instance SegmentationObject	—Unverified
Detailed Evaluation of Modern Machine Learning Approaches for Optic Plastics Sorting	May 22, 2025	Instance SegmentationObject Recognition	—Unverified
DetectFusion: Detecting and Segmenting Both Known and Unknown Dynamic Objects in Real-time SLAM	Jul 22, 2019	2D Object DetectionInstance Segmentation	—Unverified
Detecting Reflections by Combining Semantic and Instance Segmentation	Apr 30, 2019	Instance SegmentationPanoptic Segmentation	—Unverified
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning	Aug 7, 2018	Defect Detectionimage-classification	—Unverified
DetNet: Design Backbone for Object Detection	Sep 1, 2018	ClassificationGeneral Classification	—Unverified
DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation	Dec 11, 2023	Instance SegmentationMulti-Task Learning	—Unverified
Dilated SpineNet for Semantic Segmentation	Mar 23, 2021	Instance SegmentationSegmentation	—Unverified
Dimensions of Motion: Monocular Prediction through Flow Subspaces	Dec 2, 2021	Depth EstimationDepth Prediction	—Unverified
DIS-Mine: Instance Segmentation for Disaster-Awareness in Poor-Light Condition in Underground Mines	Nov 20, 2024	Instance Segmentationobject-detection	—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