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CILF-CIAE: CLIP-driven image-language fusion for correcting inverse age estimation.

Yuntao Shou1, Tao Meng1, Wei Ai1

  • 1College of Computer and Mathematics, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China.

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|January 3, 2026
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Summary

This study introduces a novel CLIP-driven method for accurate facial age estimation, significantly reducing errors by fusing image and text features with an efficient Transformer architecture and error feedback mechanisms.

Keywords:
Age estimationCLIPError correctionFourier transformImage-language fusionTransformer

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Facial age estimation is crucial for applications like age verification and access control.
  • Existing Transformer models for age estimation face high memory demands due to quadratic complexity.
  • Contrastive Language-Image Pre-training (CLIP) shows promise but its integration with error feedback for age estimation is unexplored.

Purpose of the Study:

  • To propose a novel CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation (CILF-CIAE).
  • To address memory inefficiency in Transformer-based age estimation.
  • To enhance the accuracy and robustness of age estimation systems.

Main Methods:

  • Utilized CLIP for extracting aligned image and text features.
  • Developed FourierFormer, a linear log complexity Transformer architecture for efficient image-text fusion.
  • Implemented contrastive multimodal learning to bridge the semantic gap between modalities.
  • Introduced reversible age estimation with end-to-end error feedback.

Main Results:

  • CILF-CIAE demonstrated superior performance across six benchmark datasets.
  • Achieved a Mean Absolute Error (MAE) of 1.68 on MORPH-S2, outperforming LRA-GNN (2.21) and MCGRL (1.77).
  • The proposed FourierFormer offers significant memory savings compared to traditional attention mechanisms.

Conclusions:

  • CILF-CIAE effectively integrates CLIP and an efficient Transformer for robust age estimation.
  • The method significantly reduces age prediction errors through advanced fusion and feedback techniques.
  • The findings highlight the potential of CILF-CIAE for real-world age estimation applications.