# Correlating Edge, Pose with Parsing

arXiv: https://arxiv.org/abs/2005.01431 · HTML: https://mlsanity.com/paper/2005.01431/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 18 of 18)

- [Repurposing GANs for One-shot Semantic Part Segmentation](https://mlsanity.com/paper/2103.04379/) (2021)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing](https://mlsanity.com/paper/2103.04570/) (2021)
- [Human De-occlusion: Invisible Perception and Recovery for Humans](https://mlsanity.com/paper/2103.11597/) (2021)
- [Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting](https://mlsanity.com/paper/2107.03576/) (2021)
- [CDGNet: Class Distribution Guided Network for Human Parsing](https://mlsanity.com/paper/2111.14173/) (2021)
- [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://mlsanity.com/paper/2301.00394/) (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)
- [Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures](https://mlsanity.com/paper/2210.01887/) (2022)
- [POISE: Pose Guided Human Silhouette Extraction under Occlusions](https://mlsanity.com/paper/2311.05077/) (2023)
- [End-to-end One-shot Human Parsing](https://mlsanity.com/paper/2105.01241/) (2021)
- [SCHNet: SAM Marries CLIP for Human Parsing](https://mlsanity.com/paper/2503.22237/) (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)
- [POSTURE: Pose Guided Unsupervised Domain Adaptation for Human Body Part Segmentation](https://mlsanity.com/paper/2407.03549/) (2024)
- [Nondiscriminatory Treatment: a straightforward framework for multi-human parsing](https://mlsanity.com/paper/2101.10913/) (2021)

## Abstract (arXiv)

According to existing studies, human body edge and pose are two beneficial factors to human parsing. The effectiveness of each of the high-level features (edge and pose) is confirmed through the concatenation of their features with the parsing features. Driven by the insights, this paper studies how human semantic boundaries and keypoint locations can jointly improve human 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. To capture such correlations, we propose a Correlation Parsing Machine (CorrPM) employing a heterogeneous non-local block to discover the spatial affinity among feature maps from the edge, pose and parsing. The proposed CorrPM allows us to report new state-of-the-art accuracy on three human parsing datasets. Importantly, comparative studies confirm the advantages of feature correlation over the concatenation.