# Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing

arXiv: https://arxiv.org/abs/1911.12053 · HTML: https://mlsanity.com/paper/1911.12053/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 23 of 23)

- [4D-DRESS: A 4D Dataset of Real-world Human Clothing with Semantic Annotations](https://mlsanity.com/paper/2404.18630/) (2024)
- [Learning Visual Affordance Grounding from Demonstration Videos](https://mlsanity.com/paper/2108.05675/) (2021)
- [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://mlsanity.com/paper/2301.00394/) (2023)
- [AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing](https://mlsanity.com/paper/2101.07034/) (2021)
- [FlexNeRF: Photorealistic Free-viewpoint Rendering of Moving Humans from Sparse Views](https://mlsanity.com/paper/2303.14368/) (2023)
- [AIParsing: Anchor-free Instance-level Human Parsing](https://mlsanity.com/paper/2207.06854/) (2022)
- [Quality-Aware Network for Human Parsing](https://mlsanity.com/paper/2103.05997/) (2021)
- [Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains](https://mlsanity.com/paper/2304.04140/) (2023)
- [PG-VTON: A Novel Image-Based Virtual Try-On Method via Progressive Inference Paradigm](https://mlsanity.com/paper/2304.08956/) (2023)
- [End-to-end One-shot Human Parsing](https://mlsanity.com/paper/2105.01241/) (2021)
- [Describe me if you can! Characterized Instance-level Human Parsing](https://mlsanity.com/paper/2201.09594/) (2022)
- [Parsing Objects at a Finer Granularity: A Survey](https://mlsanity.com/paper/2212.13693/) (2022)
- [Merging Multiple Datasets for Improved Appearance-Based Gaze Estimation](https://mlsanity.com/paper/2409.00912/) (2024)
- [DINOv2 Driven Gait Representation Learning for Video-Based Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2511.04281/) (2025)
- [Deep Learning for Human Parsing: A Survey](https://mlsanity.com/paper/2301.12416/) (2023)
- [Exploring the Robustness of Human Parsers Towards Common Corruptions](https://mlsanity.com/paper/2309.00938/) (2023)
- [A Novel Garment Transfer Method Supervised by Distilled Knowledge of Virtual Try-on Model](https://mlsanity.com/paper/2401.12433/) (2024)
- [Automated Sperm Morphology Analysis Based on Instance-Aware Part Segmentation](https://mlsanity.com/paper/2408.00112/) (2024)
- [Deeply Dual Supervised learning for melanoma recognition](https://mlsanity.com/paper/2508.01994/) (2025)
- [Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsampling](https://mlsanity.com/paper/2508.06805/) (2025)
- [DualResolution Residual Architecture with Artifact Suppression for Melanocytic Lesion Segmentation](https://mlsanity.com/paper/2508.06816/) (2025)
- [VesselRW: Weakly Supervised Subcutaneous Vessel Segmentation via Learned Random Walk Propagation](https://mlsanity.com/paper/2508.06819/) (2025)
- [Context-Aware Mutual Learning for Blind Image Inpainting and Beyond](https://mlsanity.com/paper/2609.14439/) (2026)

## Abstract (arXiv)

Human parsing, or human body part semantic segmentation, has been an active research topic due to its wide potential applications. 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. Starting from the prior knowledge of the human body hierarchical structure, we devise a graph pyramid module (GPM) by stacking three levels of graph structures from coarse granularity to fine granularity subsequently. At each level, GPM utilizes the self-attention mechanism to model the correlations between context nodes. Then, it adopts a top-down mechanism to progressively refine the hierarchical features through all the levels. GPM also enables efficient mutual learning. Specifically, the network weights of the first two levels are shared to exchange the learned coarse-granularity information across different datasets. By making use of the multi-granularity labels, Grapy-ML learns a more discriminative feature representation and achieves state-of-the-art performance, which is demonstrated by extensive experiments on the three popular benchmarks, e.g. CIHP dataset. The source code is publicly available at https://github.com/Charleshhy/Grapy-ML.