CZL-CIAE: CLIP-driven Zero-shot Learning for Correcting Inverse Age
  Estimation
Yuntao Shou Wei Ai Tao Meng Keqin Li
Zero-shot age estimation aims to learn feature information about age from
input images and make inferences about a given person's image or video frame
without specific sample data. The development of zero-shot age estimation can
improve the efficiency and accuracy of various applications (e.g., age
verification and secure access control, etc.), while also promoting research on
multi-modal and zero-shot learning in the social media field. For example,
zero-sample age estimation can be used to create social networks focused on
specific age groups. However, existing methods mainly focus on supervised,
labeled age estimation learning, and the prediction effect of zero-shot
learning is very poor. To tackle the above issues, we propose a novel
CLIP-driven Zero-shot Learning for Correcting Inverse Age Estimation
(CZL-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.
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, CZL-CIAE has achieved better age prediction
results.