Human Instance Segmentation
3 papers with code • 1 benchmarks • 3 datasets
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
Benchmarks
These leaderboards are used to track progress in Human Instance Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
OCHuman
TikTok Dataset
PeopleSansPeople
Most implemented papers
Pose2Seg: Detection Free Human Instance Segmentation
liruilong940607/Pose2Seg
•
•
CVPR 2019
We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem, and can moreover better handle occlusion.
Fashion-Guided Adversarial Attack on Person Segmentation
nii-yamagishilab/fashion_adv
•
•
It generates adversarial textures learned from fashion style images and then overlays them on the clothing regions in the original image to make all persons in the image invisible to person segmentation networks.
Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation
levan92/occlusion-copy-paste
•
•
In our work, we propose a simple yet effective data-centric approach, Occlusion Copy & Paste, to introduce occluded examples to models during training - we tailor the general copy & paste augmentation approach to tackle the difficult problem of same-class occlusion.
