# Leveraging Text Guidance for Enhancing Demographic Fairness in Gender Classification

arXiv: https://arxiv.org/abs/2512.11015 · HTML: https://mlsanity.com/paper/2512.11015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 57 references that are in the map.
- First posted on arXiv 11 Dec 2025.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [FLAVA: A Foundational Language And Vision Alignment Model](https://mlsanity.com/paper/2112.04482/) (2021)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [REPAIR: Removing Representation Bias by Dataset Resampling](https://mlsanity.com/paper/1904.07911/) (2019)
- [Fair Attribute Classification through Latent Space De-biasing](https://mlsanity.com/paper/2012.01469/) (2020)
- [Fair Mixup: Fairness via Interpolation](https://mlsanity.com/paper/2103.06503/) (2021)
- [FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age](https://mlsanity.com/paper/1908.04913/) (2019)
- [Fair Contrastive Learning for Facial Attribute Classification](https://mlsanity.com/paper/2203.16209/) (2022)
- [Towards causal benchmarking of bias in face analysis algorithms](https://mlsanity.com/paper/2007.06570/) (2020)
- [Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers](https://mlsanity.com/paper/2203.04913/) (2022)
- [Analysis of Gender Inequality In Face Recognition Accuracy](https://mlsanity.com/paper/2002.00065/) (2020)
- [Text-guided Foundation Model Adaptation for Pathological Image Classification](https://mlsanity.com/paper/2307.14901/) (2023)
- [GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection](https://mlsanity.com/paper/2207.10246/) (2022)
- [FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification](https://mlsanity.com/paper/2207.10888/) (2022)
- [Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups](https://mlsanity.com/paper/2009.11491/) (2020)
- [Anatomizing Bias in Facial Analysis](https://mlsanity.com/paper/2112.06522/) (2021)
- [Age Group and Gender Estimation in the Wild with Deep RoR Architecture](https://mlsanity.com/paper/1710.02985/) (2017)
- [An Examination of Bias of Facial Analysis based BMI Prediction Models](https://mlsanity.com/paper/2204.10262/) (2022)
- [Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups](https://mlsanity.com/paper/2208.08382/) (2022)
- [BWCFace: Open-set Face Recognition using Body-worn Camera](https://mlsanity.com/paper/2009.11458/) (2020)
- [Is Gender "In-the-Wild" Inference Really a Solved Problem?](https://mlsanity.com/paper/2105.05794/) (2021)
- [Towards Reducing Bias in Gender Classification](https://mlsanity.com/paper/1911.08556/) (2019)
- [Fairness Deconstructed: A Sociotechnical View of 'Fair' Algorithms in Criminal Justice](https://mlsanity.com/paper/2106.13455/) (2021)

## Abstract (arXiv)

In the quest for fairness in artificial intelligence, novel approaches to enhance it in facial image based gender classification algorithms using text guided methodologies are presented. The core methodology involves leveraging semantic information from image captions during model training to improve generalization capabilities. Two key strategies are presented: Image Text Matching (ITM) guidance and Image Text fusion. ITM guidance trains the model to discern fine grained alignments between images and texts to obtain enhanced multimodal representations. Image text fusion combines both modalities into comprehensive representations for improved fairness. Exensive experiments conducted on benchmark datasets demonstrate these approaches effectively mitigate bias and improve accuracy across gender racial groups compared to existing methods. Additionally, the unique integration of textual guidance underscores an interpretable and intuitive training paradigm for computer vision systems. By scrutinizing the extent to which semantic information reduces disparities, this research offers valuable insights into cultivating more equitable facial analysis algorithms. The proposed methodologies contribute to addressing the pivotal challenge of demographic bias in gender classification from facial images. Furthermore, this technique operates in the absence of demographic labels and is application agnostic.