Human parsing is the task of segmenting a human image into different fine-grained semantic parts such as head, torso, arms and legs.
( Image credit: Multi-Human-Parsing (MHP) )
Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage.
Ranked #32 on Semantic Segmentation on Cityscapes test
Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc.
Ranked #1 on Multi-Human Parsing on MHP v2.0
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.
Ranked #1 on Semantic Segmentation on LIP val
To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality.
Ranked #7 on Semantic Segmentation on LIP val
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.
Ranked #3 on Human Part Segmentation on CIHP
In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations.
Ranked #4 on Human Part Segmentation on PASCAL-Part (using extra training data)
Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance.
Ranked #1 on Human Part Segmentation on MHP v2.0
Human parsing has recently attracted a lot of research interests due to its huge application potentials.
Ranked #10 on Semantic Segmentation on LIP val