Human Parsing
56 papers with code • 1 benchmarks • 2 datasets
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) )
Benchmarks
These leaderboards are used to track progress in Human Parsing
| Trend | Dataset | Best Model | Paper | Code | Compare |
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PASCAL Context
MHP
Most implemented papers
Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
HCPLab-SYSU/ATEN
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Beyond the existing single-person and multiple-person human parsing tasks in static images, this paper makes the first attempt to investigate a more realistic video instance-level human parsing that simultaneously segments out each person instance and parses each instance into more fine-grained parts (e. g., head, leg, dress).
Self-Supervised Learning via Conditional Motion Propagation
XiaohangZhan/conditional-motion-propagation
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CVPR 2019
Instead of explicitly modeling the motion probabilities, we design the pretext task as a conditional motion propagation problem.
Graphonomy: Universal Human Parsing via Graph Transfer Learning
Gaoyiminggithub/Graphonomy
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CVPR 2019
By distilling universal semantic graph representation to each specific task, Graphonomy is able to predict all levels of parsing labels in one system without piling up the complexity.
Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification
ggjy/P2Net.pytorch
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ICCV 2019
On the other hand, there still exist many useful contextual cues that do not fall into the scope of predefined human parts or attributes.
Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing
In this paper, we propose a novel GRAph PYramid Mutual Learning (Grapy-ML) method to address the cross-dataset human parsing problem, where the annotations are at different granularities.
Learning Compositional Neural Information Fusion for Human Parsing
ZzzjzzZ/CompositionalHumanParsing
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ICCV 2019
The bottom-up and top-down inferences explicitly model the compositional and decompositional relations in human bodies, respectively.
C-DLinkNet: considering multi-level semantic features for human parsing
Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human.
Hierarchical Human Parsing with Typed Part-Relation Reasoning
hlzhu09/Hierarchical-Human-Parsing
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CVPR 2020
As human bodies are underlying hierarchically structured, how to model human structures is the central theme in this task.
Correlating Edge, Pose with Parsing
Compared with the existing practice of feature concatenation, we find that uncovering the correlation among the three factors is a superior way of leveraging the pivotal contextual cues provided by edges and poses.
Identity-Guided Human Semantic Parsing for Person Re-Identification
CASIA-IVA-Lab/ISP-reID
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ECCV 2020
In this paper, we propose the identity-guided human semantic parsing approach (ISP) to locate both the human body parts and personal belongings at pixel-level for aligned person re-ID only with person identity labels.
