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
X^3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection	Mar 3, 2023	3D Object DetectionInstance Segmentation	—Unverified
X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection	Jan 1, 2023	3D Object DetectionInstance Segmentation	—Unverified
X-volution: On the unification of convolution and self-attention	Jun 4, 2021	Image ClassificationInstance Segmentation	—Unverified
X-World: Accessibility, Vision, and Autonomy Meet	Jan 1, 2021	Instance SegmentationPrivacy Preserving	—Unverified
YOLO2U-Net: Detection-Guided 3D Instance Segmentation for Microscopy	Jul 13, 2022	3D Instance SegmentationInstance Segmentation	—Unverified
You Only Click Once: Single Point Weakly Supervised 3D Instance Segmentation for Autonomous Driving	Feb 27, 2025	3D Instance SegmentationAutonomous Driving	—Unverified
You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping	Jun 6, 2025	6D Pose EstimationInstance Segmentation	—Unverified
ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation	Nov 24, 2023	3D Part SegmentationInstance Segmentation	—Unverified
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models	Jun 15, 2023	Anomaly DetectionInstance Segmentation	—Unverified
Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development	Nov 18, 2024	Instance SegmentationLarge Language Model	—Unverified
Zero-Shot Dual-Path Integration Framework for Open-Vocabulary 3D Instance Segmentation	Aug 16, 2024	3D Instance SegmentationInstance Segmentation	—Unverified
ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation	Jun 1, 2019	Instance SegmentationSegmentation	—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