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Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
3D INSTANCE SEGMENTATION HUMAN PART SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING MULTI-PERSON POSE ESTIMATION MULTI-TISSUE NUCLEUS SEGMENTATION NUCLEAR SEGMENTATION PANOPTIC SEGMENTATION REAL-TIME OBJECT DETECTION
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.
#4 best model for Human Part Segmentation on PASCAL-Person-Part (using extra training data)
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
To tackle the problem of learning with label noises, this work introduces a purification strategy, called Self-Correction for Human Parsing (SCHP), to progressively promote the reliability of the supervised labels as well as the learned models.
SOTA for Semantic Segmentation on LIP val
Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance.
SOTA for Human Part Segmentation on CIHP
On the other hand, if part labels are also available in the real-images during training, our method outperforms the supervised state-of-the-art methods by a large margin.
SOTA for Human Part Segmentation on PASCAL-Person-Part (using extra training data)