# VM-BeautyNet: A Synergistic Ensemble of Vision Transformer and Mamba for Facial Beauty Prediction

arXiv: https://arxiv.org/abs/2510.16220 · HTML: https://mlsanity.com/paper/2510.16220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Vision Mamba backbones, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Vision Mamba backbones](https://mlsanity.com/ssm/vision-mamba-backbones/)

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction](https://mlsanity.com/paper/1801.06345/) (2018)
- [Label distribution based facial attractiveness computation by deep residual learning](https://mlsanity.com/paper/1609.00496/) (2016)
- [Generative Pre-training for Subjective Tasks: A Diffusion Transformer-Based Framework for Facial Beauty Prediction](https://mlsanity.com/paper/2507.20363/) (2025)
- [Mamba-CNN: A Hybrid Architecture for Efficient and Accurate Facial Beauty Prediction](https://mlsanity.com/paper/2509.01431/) (2025)
- [Scale-interaction transformer: a hybrid cnn-transformer model for facial beauty prediction](https://mlsanity.com/paper/2509.05078/) (2025)
- [SynergyNet: Fusing Generative Priors and State-Space Models for Facial Beauty Prediction](https://mlsanity.com/paper/2509.17172/) (2025)
- [FairViT-GAN: A Hybrid Vision Transformer with Adversarial Debiasing for Fair and Explainable Facial Beauty Prediction](https://mlsanity.com/paper/2509.23859/) (2025)

## Abstract (arXiv)

Facial Beauty Prediction (FBP) is a complex and challenging computer vision task, aiming to model the subjective and intricate nature of human aesthetic perception. While deep learning models, particularly Convolutional Neural Networks (CNNs), have made significant strides, they often struggle to capture the global, holistic facial features that are critical to human judgment. Vision Transformers (ViT) address this by effectively modeling long-range spatial relationships, but their quadratic complexity can be a bottleneck. This paper introduces a novel, heterogeneous ensemble architecture, VM-BeautyNet, that synergistically fuses the complementary strengths of a Vision Transformer and a Mamba-based Vision model, a recent advancement in State-Space Models (SSMs). The ViT backbone excels at capturing global facial structure and symmetry, while the Mamba backbone efficiently models long-range dependencies with linear complexity, focusing on sequential features and textures. We evaluate our approach on the benchmark SCUT-FBP5500 dataset. Our proposed VM-BeautyNet achieves state-of-the-art performance, with a Pearson Correlation (PC) of 0.9212, a Mean Absolute Error (MAE) of 0.2085, and a Root Mean Square Error (RMSE) of 0.2698. Furthermore, through Grad-CAM visualizations, we provide interpretability analysis that confirms the complementary feature extraction of the two backbones, offering new insights into the model's decision-making process and presenting a powerful new architectural paradigm for computational aesthetics.