# Learning Semantic Neural Tree for Human Parsing

arXiv: https://arxiv.org/abs/1912.09622 · HTML: https://mlsanity.com/paper/1912.09622/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 29 of 29)

- [Semantic-SAM: Segment and Recognize Anything at Any Granularity](https://mlsanity.com/paper/2307.04767/) (2023)
- [Deep Hierarchical Semantic Segmentation](https://mlsanity.com/paper/2203.14335/) (2022)
- [UniHCP: A Unified Model for Human-Centric Perceptions](https://mlsanity.com/paper/2303.02936/) (2023)
- [Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation](https://mlsanity.com/paper/2204.04655/) (2022)
- [Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing](https://mlsanity.com/paper/2103.04570/) (2021)
- [Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark](https://mlsanity.com/paper/2108.07020/) (2021)
- [CDGNet: Class Distribution Guided Network for Human Parsing](https://mlsanity.com/paper/2111.14173/) (2021)
- [Hulk: A Universal Knowledge Translator for Human-Centric Tasks](https://mlsanity.com/paper/2312.01697/) (2023)
- [PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation](https://mlsanity.com/paper/2301.00954/) (2023)
- [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://mlsanity.com/paper/2301.00394/) (2023)
- [PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection](https://mlsanity.com/paper/2211.10763/) (2022)
- [LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning](https://mlsanity.com/paper/2309.13556/) (2023)
- [Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition](https://mlsanity.com/paper/2204.04654/) (2022)
- [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)
- [End-to-end One-shot Human Parsing](https://mlsanity.com/paper/2105.01241/) (2021)
- [CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models](https://mlsanity.com/paper/2412.19331/) (2024)
- [Describe me if you can! Characterized Instance-level Human Parsing](https://mlsanity.com/paper/2201.09594/) (2022)
- [RepParser: End-to-End Multiple Human Parsing with Representative Parts](https://mlsanity.com/paper/2208.12908/) (2022)
- [UniParser: Multi-Human Parsing with Unified Correlation Representation Learning](https://mlsanity.com/paper/2310.08984/) (2023)
- [Parsing Objects at a Finer Granularity: A Survey](https://mlsanity.com/paper/2212.13693/) (2022)
- [Single-stage Multi-human Parsing via Point Sets and Center-based Offsets](https://mlsanity.com/paper/2304.11356/) (2023)
- [SCHNet: SAM Marries CLIP for Human Parsing](https://mlsanity.com/paper/2503.22237/) (2025)
- [Hierarchical Pyramid Representations for Semantic Segmentation](https://mlsanity.com/paper/2104.01792/) (2021)
- [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)
- [Nondiscriminatory Treatment: a straightforward framework for multi-human parsing](https://mlsanity.com/paper/2101.10913/) (2021)
- [CIParsing: Unifying Causality Properties into Multiple Human Parsing](https://mlsanity.com/paper/2308.12218/) (2023)

## Abstract (arXiv)

The majority of existing human parsing methods formulate the task as semantic segmentation, which regard each semantic category equally and fail to exploit the intrinsic physiological structure of human body, resulting in inaccurate results. In this paper, we design a novel semantic neural tree for human parsing, which uses a tree architecture to encode physiological structure of human body, and designs a coarse to fine process in a cascade manner to generate accurate results. Specifically, the semantic neural tree is designed to segment human regions into multiple semantic subregions (e.g., face, arms, and legs) in a hierarchical way using a new designed attention routing module. Meanwhile, we introduce the semantic aggregation module to combine multiple hierarchical features to exploit more context information for better performance. Our semantic neural tree can be trained in an end-to-end fashion by standard stochastic gradient descent (SGD) with back-propagation. Several experiments conducted on four challenging datasets for both single and multiple human parsing, i.e., LIP, PASCAL-Person-Part, CIHP and MHP-v2, demonstrate the effectiveness of the proposed method. Code can be found at https://isrc.iscas.ac.cn/gitlab/research/sematree.