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 1111–1120 of 2262 papers

Title	Date	Tasks	Status	Score
MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception	Jun 15, 2024	Autonomous Drivingenergy management	CodeCode Available	5
Learning Instance Segmentation by Interaction	Jun 21, 2018	Instance SegmentationSegmentation	CodeCode Available	5
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming	Jul 17, 2019	Autonomous DrivingBenchmarking	CodeCode Available	5
Learning Instance Occlusion for Panoptic Segmentation	Jun 13, 2019	Instance SegmentationPanoptic Segmentation	CodeCode Available	5
Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters	Jun 16, 2024	BenchmarkingInstance Segmentation	CodeCode Available	5
Accurate Fine-grained Layout Analysis for the Historical Tibetan Document Based on the Instance Segmentation	Oct 15, 2021	Instance SegmentationSemantic Segmentation	CodeCode Available	5
Learning deep structured active contours end-to-end	Mar 16, 2018	Instance SegmentationSegmentation	CodeCode Available	5
Deep Level Set for Box-supervised Instance Segmentation in Aerial Images	Dec 7, 2021	Box-supervised Instance SegmentationInstance Segmentation	CodeCode Available	5
Learning a Spatio-Temporal Embedding for Video Instance Segmentation	Dec 19, 2019	Instance SegmentationSemantic Segmentation	CodeCode Available	5
Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak Labels	Aug 9, 2023	Instance SegmentationPathfinder	CodeCode Available	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