CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation

Understand

The age estimation task aims to predict the age of an individual by analyzing facial features in an image.

• The development of age estimation can improve the efficiency and accuracy of various applications (e.g., age verification and secure access control, etc.).
• In recent years, contrastive language-image pre-training (CLIP) has been widely used in various multimodal tasks and has made some progress in the field of age estimation.
• However, existing CLIP-based age estimation methods require high memory usage (quadratic complexity) when globally modeling images, and lack an error feedback mechanism to prompt the model about the quality of age prediction results.