# Masked Contrastive Graph Representation Learning for Age Estimation

arXiv: https://arxiv.org/abs/2306.17798 · HTML: https://mlsanity.com/paper/2306.17798/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 62 references that are in the map.
- First posted on arXiv 16 Jun 2023.

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

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://mlsanity.com/paper/1711.09869/) (2017)
- [Vision GNN: An Image is Worth Graph of Nodes](https://mlsanity.com/paper/2206.00272/) (2022)
- [Towards Robust Vision Transformer](https://mlsanity.com/paper/2105.07926/) (2021)
- [Rank consistent ordinal regression for neural networks with application to age estimation](https://mlsanity.com/paper/1901.07884/) (2019)
- [CondenseNet: An Efficient DenseNet using Learned Group Convolutions](https://mlsanity.com/paper/1711.09224/) (2017)
- [Deep Label Distribution Learning with Label Ambiguity](https://mlsanity.com/paper/1611.01731/) (2016)
- [What Can ResNet Learn Efficiently, Going Beyond Kernels?](https://mlsanity.com/paper/1905.10337/) (2019)
- [Contrastive Self-supervised Learning for Graph Classification](https://mlsanity.com/paper/2009.05923/) (2020)
- [Moving Window Regression: A Novel Approach to Ordinal Regression](https://mlsanity.com/paper/2203.13122/) (2022)
- [OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression](https://mlsanity.com/paper/2206.02338/) (2022)
- [BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation](https://mlsanity.com/paper/1904.03358/) (2019)
- [Deep Regression Forests for Age Estimation](https://mlsanity.com/paper/1712.07195/) (2017)
- [Bipartite Graph Embedding via Mutual Information Maximization](https://mlsanity.com/paper/2012.05442/) (2020)
- [C3AE: Exploring the Limits of Compact Model for Age Estimation](https://mlsanity.com/paper/1904.05059/) (2019)
- [Fine-Grained Age Estimation in the wild with Attention LSTM Networks](https://mlsanity.com/paper/1805.10445/) (2018)
- [Deep Differentiable Random Forests for Age Estimation](https://mlsanity.com/paper/1907.10665/) (2019)
- [Quantifying Facial Age by Posterior of Age Comparisons](https://mlsanity.com/paper/1708.09687/) (2017)
- [Improving Face-Based Age Estimation with Attention-Based Dynamic Patch Fusion](https://mlsanity.com/paper/2112.10167/) (2021)
- [Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation](https://mlsanity.com/paper/1601.07977/) (2016)

## Built on by (most cited first, 27 of 27)

- [Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck](https://mlsanity.com/paper/2408.00295/) (2024)
- [SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples](https://mlsanity.com/paper/2410.10365/) (2024)
- [Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction](https://mlsanity.com/paper/2411.14001/) (2024)
- [Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation](https://mlsanity.com/paper/2407.16714/) (2024)
- [CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation](https://mlsanity.com/paper/2312.01758/) (2023)
- [Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation](https://mlsanity.com/paper/2403.02302/) (2024)
- [MCSFF: Multi-modal Consistency and Specificity Fusion Framework for Entity Alignment](https://mlsanity.com/paper/2410.14584/) (2024)
- [Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification](https://mlsanity.com/paper/2410.18130/) (2024)
- [A Multi-view Mask Contrastive Learning Graph Convolutional Neural Network for Age Estimation](https://mlsanity.com/paper/2407.16234/) (2024)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment](https://mlsanity.com/paper/2410.20733/) (2024)
- [From Age Estimation to Age-Invariant Face Recognition: Generalized Age Feature Extraction Using Order-Enhanced Contrastive Learning](https://mlsanity.com/paper/2501.01760/) (2025)
- [GroupFace: Imbalanced Age Estimation Based on Multi-hop Attention Graph Convolutional Network and Group-aware Margin Optimization](https://mlsanity.com/paper/2412.11450/) (2024)
- [Dual-branch Graph Domain Adaptation for Cross-scenario Multi-modal Emotion Recognition](https://mlsanity.com/paper/2603.26840/) (2026)
- [SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation](https://mlsanity.com/paper/2412.11652/) (2024)
- [LRA-GNN: Latent Relation-Aware Graph Neural Network with Initial and Dynamic Residual for Facial Age Estimation](https://mlsanity.com/paper/2502.05423/) (2025)
- [Overcoming Occlusions in the Wild: A Multi-Task Age Head Approach to Age Estimation](https://mlsanity.com/paper/2506.13445/) (2025)
- [A Flow Model with Low-Rank Transformers for Incomplete Multimodal Survival Analysis](https://mlsanity.com/paper/2510.21829/) (2025)
- [Focal-RegionFace: Generating Fine-Grained Multi-attribute Descriptions for Arbitrarily Selected Face Focal Regions](https://mlsanity.com/paper/2601.00156/) (2026)
- [SE-GNN: Seed Expanded-Aware Graph Neural Network with Iterative Optimization for Semi-supervised Entity Alignment](https://mlsanity.com/paper/2503.20801/) (2025)
- [TimeGNN-Augmented Hybrid-Action MARL for Fine-Grained Task Partitioning and Energy-Aware Offloading in MEC](https://mlsanity.com/paper/2601.06191/) (2026)
- [AMB-DSGDN: Adaptive Modality-Balanced Dynamic Semantic Graph Differential Network for Multimodal Emotion Recognition](https://mlsanity.com/paper/2603.10043/) (2026)
- [Dynamic Fusion-Aware Graph Convolutional Neural Network for Multimodal Emotion Recognition in Conversations](https://mlsanity.com/paper/2603.22345/) (2026)
- [Relational graph-driven differential denoising and diffusion attention fusion for multimodal conversation emotion recognition](https://mlsanity.com/paper/2603.25752/) (2026)
- [Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognition](https://mlsanity.com/paper/2604.14204/) (2026)
- [MAGE-Vein: Multi-Instance Age and Gender Estimation from Finger Vein Images](https://mlsanity.com/paper/2607.20897/) (2026)
- [C2MOE: Consistency and Complementarity-guided Mixture of Experts for Incomplete Multimodal Emotion Learning](https://mlsanity.com/paper/2608.04013/) (2026)

## Abstract (arXiv)

Age estimation of face images is a crucial task with various practical applications in areas such as video surveillance and Internet access control. While deep learning-based age estimation frameworks, e.g., convolutional neural network (CNN), multi-layer perceptrons (MLP), and transformers have shown remarkable performance, they have limitations when modelling complex or irregular objects in an image that contains a large amount of redundant information. To address this issue, this paper utilizes the robustness property of graph representation learning in dealing with image redundancy information and proposes a novel Masked Contrastive Graph Representation Learning (MCGRL) method for age estimation. Specifically, our approach first leverages CNN to extract semantic features of the image, which are then partitioned into patches that serve as nodes in the graph. Then, we use a masked graph convolutional network (GCN) to derive image-based node representations that capture rich structural information. Finally, we incorporate multiple losses to explore the complementary relationship between structural information and semantic features, which improves the feature representation capability of GCN. Experimental results on real-world face image datasets demonstrate the superiority of our proposed method over other state-of-the-art age estimation approaches.