|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
SOTA for Instance Segmentation on Cityscapes (using extra training data)
In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data.
In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations.
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.
#2 best model for Human Part Segmentation on CIHP
Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance.
SOTA for Pose Estimation on DensePose-COCO