SOTAVerified

Human Instance Segmentation

Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.

Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers

Papers

Showing 110 of 16 papers

TitleStatusHype
Detection, Pose Estimation and Segmentation for Multiple Bodies: Closing the Virtuous CircleCode3
Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded ScenesCode2
Occlusion-Aware Instance Segmentation via BiLayer Network ArchitecturesCode2
You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric PerceptionCode1
Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance SegmentationCode1
Contextual Instance Decoupling for Robust Multi-Person Pose EstimationCode1
Real-time Human-Centric Segmentation for Complex Video ScenesCode1
Object-Centric Multi-Task Learning for Human Instances0
Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation0
Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BBox-Mask-Pose 2xAP32.4Unverified
2Crowd-SAM (ViT-L)AP31.4Unverified
3HQNet (ResNet-50)AP31.1Unverified
4BlendMask + CISAP29.8Unverified
5Mask2Former + Occlusion C&PAP28.3Unverified
6CondInst + CISAP28.1Unverified
7Mask2FormerAP27.8Unverified
8HCQNetAP27.3Unverified
9ExPoSegAP26.8Unverified
10RTMDet-ins-lAP26.5Unverified