# Improving Facial Attribute Recognition by Group and Graph Learning

arXiv: https://arxiv.org/abs/2105.13825 · HTML: https://mlsanity.com/paper/2105.13825/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 17 of 17)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification](https://mlsanity.com/paper/1611.05377/) (2016)
- [Dynamic Curriculum Learning for Imbalanced Data Classification](https://mlsanity.com/paper/1901.06783/) (2019)
- [Deep Imbalanced Learning for Face Recognition and Attribute Prediction](https://mlsanity.com/paper/1806.00194/) (2018)
- [Imbalanced Deep Learning by Minority Class Incremental Rectification](https://mlsanity.com/paper/1804.10851/) (2018)
- [Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach](https://mlsanity.com/paper/1706.00906/) (2017)
- [MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes](https://mlsanity.com/paper/1603.07027/) (2016)
- [PANDA: Pose Aligned Networks for Deep Attribute Modeling](https://mlsanity.com/paper/1311.5591/) (2013)
- [GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning](https://mlsanity.com/paper/1804.06964/) (2018)
- [Improving Facial Attribute Prediction using Semantic Segmentation](https://mlsanity.com/paper/1704.08740/) (2017)
- [Hierarchical Feature Embedding for Attribute Recognition](https://mlsanity.com/paper/2005.11576/) (2020)
- [Recognizing Part Attributes with Insufficient Data](https://mlsanity.com/paper/1908.03335/) (2019)
- [Face Attribute Prediction Using Off-the-Shelf CNN Features](https://mlsanity.com/paper/1602.03935/) (2016)
- [A Deep Cascade Network for Unaligned Face Attribute Classification](https://mlsanity.com/paper/1709.03851/) (2017)
- [Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data](https://mlsanity.com/paper/1604.06433/) (2016)
- [Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition](https://mlsanity.com/paper/2007.09344/) (2020)

## Built on by (most cited first, 7 of 7)

- [UFDA: Universal Federated Domain Adaptation with Practical Assumptions](https://mlsanity.com/paper/2311.15570/) (2023)
- [An Efficient Adaptive Compression Method for Human Perception and Machine Vision Tasks](https://mlsanity.com/paper/2501.04329/) (2025)
- [Towards Point Cloud Compression for Machine Perception: A Simple and Strong Baseline by Learning the Octree Depth Level Predictor](https://mlsanity.com/paper/2406.00791/) (2024)
- [Progressive Classifier and Feature Extractor Adaptation for Unsupervised Domain Adaptation on Point Clouds](https://mlsanity.com/paper/2311.16474/) (2023)
- [ASD: Towards Attribute Spatial Decomposition for Prior-Free Facial Attribute Recognition](https://mlsanity.com/paper/2210.13716/) (2022)
- [Mask-Guided Multi-Task Network for Face Attribute Recognition](https://mlsanity.com/paper/2601.01408/) (2026)
- [FAR-AMTN: Attention Multi-Task Network for Face Attribute Recognition](https://mlsanity.com/paper/2601.01537/) (2026)

## Abstract (arXiv)

Exploiting the relationships between attributes is a key challenge for improving multiple facial attribute recognition. In this work, we are concerned with two types of correlations that are spatial and non-spatial relationships. For the spatial correlation, we aggregate attributes with spatial similarity into a part-based group and then introduce a Group Attention Learning to generate the group attention and the part-based group feature. On the other hand, to discover the non-spatial relationship, we model a group-based Graph Correlation Learning to explore affinities of predefined part-based groups. We utilize such affinity information to control the communication between all groups and then refine the learned group features. Overall, we propose a unified network called Multi-scale Group and Graph Network. It incorporates these two newly proposed learning strategies and produces coarse-to-fine graph-based group features for improving facial attribute recognition. Comprehensive experiments demonstrate that our approach outperforms the state-of-the-art methods.