  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. To tackle the above issues,
we propose a novel CLIP-driven Image-Language Fusion for Correcting Inverse Age
Estimation (CILF-CIAE). Specifically, we first introduce the CLIP model to
extract image features and text semantic information respectively, and map them
into a highly semantically aligned high-dimensional feature space. Next, we
designed a new Transformer architecture (i.e., FourierFormer) to achieve
channel evolution and spatial interaction of images, and to fuse image and text
semantic information. Compared with the quadratic complexity of the attention
mechanism, the proposed Fourierformer is of linear log complexity. To further
narrow the semantic gap between image and text features, we utilize an
efficient contrastive multimodal learning module that supervises the multimodal
fusion process of FourierFormer through contrastive loss for image-text
matching, thereby improving the interaction effect between different
modalities. Finally, we introduce reversible age estimation, which uses
end-to-end error feedback to reduce the error rate of age predictions. Through
extensive experiments on multiple data sets, CILF-CIAE has achieved better age
prediction results.
