# Global Relation Modeling and Refinement for Bottom-Up Human Pose Estimation

arXiv: https://arxiv.org/abs/2303.14888 · HTML: https://mlsanity.com/paper/2303.14888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 43 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 30 of 33)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://mlsanity.com/paper/1908.10357/) (2019)
- [Efficient Object Localization Using Convolutional Networks](https://mlsanity.com/paper/1411.4280/) (2014)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [Associative Embedding: End-to-End Learning for Joint Detection and Grouping](https://mlsanity.com/paper/1611.05424/) (2016)
- [HRFormer: High-Resolution Transformer for Dense Prediction](https://mlsanity.com/paper/2110.09408/) (2021)
- [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://mlsanity.com/paper/1803.08225/) (2018)
- [PifPaf: Composite Fields for Human Pose Estimation](https://mlsanity.com/paper/1903.06593/) (2019)
- [Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression](https://mlsanity.com/paper/2104.02300/) (2021)
- [Single-Stage Multi-Person Pose Machines](https://mlsanity.com/paper/1908.09220/) (2019)
- [Multi-Context Attention for Human Pose Estimation](https://mlsanity.com/paper/1702.07432/) (2017)
- [Learning Delicate Local Representations for Multi-Person Pose Estimation](https://mlsanity.com/paper/2003.04030/) (2020)
- [Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation](https://mlsanity.com/paper/2007.02846/) (2020)
- [Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation](https://mlsanity.com/paper/2007.11864/) (2020)
- [DirectPose: Direct End-to-End Multi-Person Pose Estimation](https://mlsanity.com/paper/1911.07451/) (2019)
- [Object detection via a multi-region & semantic segmentation-aware CNN model](https://mlsanity.com/paper/1505.01749/) (2015)
- [Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information](https://mlsanity.com/paper/1905.03466/) (2019)
- [Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation](https://mlsanity.com/paper/1911.10529/) (2019)
- [3D Human Pose Estimation with 2D Marginal Heatmaps](https://mlsanity.com/paper/1806.01484/) (2018)
- [Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation](https://mlsanity.com/paper/2205.01271/) (2022)

## Abstract (arXiv)

In this paper, we concern on the bottom-up paradigm in multi-person pose estimation (MPPE). Most previous bottom-up methods try to consider the relation of instances to identify different body parts during the post processing, while ignoring to model the relation among instances or environment in the feature learning process. In addition, most existing works adopt the operations of upsampling and downsampling. During the sampling process, there will be a problem of misalignment with the source features, resulting in deviations in the keypoint features learned by the model. To overcome the above limitations, we propose a convolutional neural network for bottom-up human pose estimation. It invovles two basic modules: (i) Global Relation Modeling (GRM) module globally learns relation (e.g., environment context, instance interactive information) among region of image by fusing multiple stages features in the feature learning process. It combines with the spatial-channel attention mechanism, which focuses on achieving adaptability in spatial and channel dimensions. (ii) Multi-branch Feature Align (MFA) module aggregates features from multiple branches to align fused feature and obtain refined local keypoint representation. Our model has the ability to focus on different granularity from local to global regions, which significantly boosts the performance of the multi-person pose estimation. Our results on the COCO and CrowdPose datasets demonstrate that it is an efficient framework for multi-person pose estimation.