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 1671–1680 of 2262 papers

Title	Date	Tasks	Status
SCR: Smooth Contour Regression with Geometric Priors	Feb 8, 2022	Instance SegmentationObject	—Unverified
SDI-Paste: Synthetic Dynamic Instance Copy-Paste for Video Instance Segmentation	Oct 16, 2024	Data AugmentationInstance Segmentation	—Unverified
SDOD:Real-time Segmenting and Detecting 3D Object by Depth	Jan 26, 2020	Autonomous DrivingInstance Segmentation	—Unverified
SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment	Mar 2, 2022	Instance Segmentationobject-detection	—Unverified
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency	Dec 2, 2021	Active LearningInstance Segmentation	—Unverified
Search3D: Hierarchical Open-Vocabulary 3D Segmentation	Sep 27, 2024	3D Instance Segmentation3D Part Segmentation	—Unverified
SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification	Nov 9, 2023	BenchmarkingInstance Segmentation	—Unverified
SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification	Nov 18, 2022	BenchmarkingInstance Segmentation	—Unverified
Neural Groundplans: Persistent Neural Scene Representations from a Single Image	Jul 22, 2022	DisentanglementInstance Segmentation	—Unverified
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation	Nov 17, 2020	Human DetectionHuman Instance Segmentation	—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