Human Part Segmentation
14 papers with code • 6 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in Human Part Segmentation
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Datasets
Human3.6M
PASCAL-Part
CIHP
MHP
Pascal Panoptic Parts
Cityscapes Panoptic Parts
TikTok Dataset
VESSEL12
CCIHP
Most implemented papers
Mask R-CNN
tensorflow/models
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ICCV 2017
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
In this technical report, we present two novel datasets for image scene understanding.
Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation
kevinlin311tw/CDCL-human-part-segmentation
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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.
Learning from Synthetic Humans
gulvarol/surreal
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CVPR 2017
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.
Parsing R-CNN for Instance-Level Human Analysis
soeaver/Parsing-R-CNN
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CVPR 2019
Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance.
Self-Correction for Human Parsing
PeikeLi/Self-Correction-Human-Parsing
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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.
Hulk: A Universal Knowledge Translator for Human-Centric Tasks
Human-centric perception tasks, e. g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports analysis.
UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory
In this work we introduce a convolutional neural network (CNN) that jointly handles low-, mid-, and high-level vision tasks in a unified architecture that is trained end-to-end.
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer
In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations.
Macro-Micro Adversarial Network for Human Parsing
To address the two kinds of inconsistencies, this paper proposes the Macro-Micro Adversarial Net (MMAN).
