# Graph-Boosted Attentive Network for Semantic Body Parsing

arXiv: https://arxiv.org/abs/2407.05924 · HTML: https://mlsanity.com/paper/2407.05924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 68 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 30 of 32)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts](https://mlsanity.com/paper/1406.2031/) (2014)
- [Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning](https://mlsanity.com/paper/2003.00392/) (2020)
- [Attention to Scale: Scale-aware Semantic Image Segmentation](https://mlsanity.com/paper/1511.03339/) (2015)
- [Video Object Segmentation with Episodic Graph Memory Networks](https://mlsanity.com/paper/2007.07020/) (2020)
- [Learning a Discriminative Feature Network for Semantic Segmentation](https://mlsanity.com/paper/1804.09337/) (2018)
- [Part-based R-CNNs for Fine-grained Category Detection](https://mlsanity.com/paper/1407.3867/) (2014)
- [Human Semantic Parsing for Person Re-identification](https://mlsanity.com/paper/1804.00216/) (2018)
- [DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model](https://mlsanity.com/paper/1605.03170/) (2016)
- [Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks](https://mlsanity.com/paper/2001.06807/) (2020)
- [Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification](https://mlsanity.com/paper/1803.09882/) (2018)
- [Semantic Object Parsing with Graph LSTM](https://mlsanity.com/paper/1603.07063/) (2016)
- [Joint Multi-Person Pose Estimation and Semantic Part Segmentation](https://mlsanity.com/paper/1708.03383/) (2017)
- [Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking](https://mlsanity.com/paper/2106.03772/) (2021)
- [Training Deeper Convolutional Networks with Deep Supervision](https://mlsanity.com/paper/1505.02496/) (2015)
- [GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval](https://mlsanity.com/paper/1709.04329/) (2017)
- [GraphFPN: Graph Feature Pyramid Network for Object Detection](https://mlsanity.com/paper/2108.00580/) (2021)
- [Joint Object and Part Segmentation using Deep Learned Potentials](https://mlsanity.com/paper/1505.00276/) (2015)
- [Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net](https://mlsanity.com/paper/1511.06881/) (2015)
- [Holistic, Instance-Level Human Parsing](https://mlsanity.com/paper/1709.03612/) (2017)
- [Learning Hierarchical Graph Neural Networks for Image Clustering](https://mlsanity.com/paper/2107.01319/) (2021)
- [Semantic Object Parsing with Local-Global Long Short-Term Memory](https://mlsanity.com/paper/1511.04510/) (2015)
- [End-to-end video instance segmentation via spatial-temporal graph neural networks](https://mlsanity.com/paper/2203.03145/) (2022)
- [Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2001.07108/) (2020)

## Abstract (arXiv)

Human body parsing remains a challenging problem in natural scenes due to multi-instance and inter-part semantic confusions as well as occlusions. This paper proposes a novel approach to decomposing multiple human bodies into semantic part regions in unconstrained environments. Specifically we propose a convolutional neural network (CNN) architecture which comprises of novel semantic and contour attention mechanisms across feature hierarchy to resolve the semantic ambiguities and boundary localization issues related to semantic body parsing. We further propose to encode estimated pose as higher-level contextual information which is combined with local semantic cues in a novel graphical model in a principled manner. In this proposed model, the lower-level semantic cues can be recursively updated by propagating higher-level contextual information from estimated pose and vice versa across the graph, so as to alleviate erroneous pose information and pixel level predictions. We further propose an optimization technique to efficiently derive the solutions. Our proposed method achieves the state-of-art results on the challenging Pascal Person-Part dataset.