视觉-语言多模态下的多任务人脸年龄估计OA北大核心CSTPCD

Existing age estimation methods are based only on face images and cannot fully utilize the linguistic contextual information behind the images.In addition,these methods usually focus on the optimization of a single age estimation task,ignoring the information brought by similar tasks to improve the model performance.To address the above problems,a multi-task face age estimation method based on vision-language multimodality is proposed,which utilizes prompt text information to provide richer and more accurate image understanding and a priori knowledge for age estimation.Meanwhile,a multi-task learning method is introduced to combine the age classification task with the ordinal regression task by utilizing the complementarity between tasks to obtain better performance.In order to obtain reliable prediction results,two multi-task result fusion methods are investigated:weighted averaging and task regression,and ablation experiments are conducted on the weighting factor of the weighted averaging method to find a suitable set of weighting factors.In comparison with the state-of-the-art methods,the mean absolute error(MAE)of the proposed method is reduced by 7.32%on the UTK-FACE dataset,its MAE is reduced by 1.20%,and its cumulative score(CS)is improved by 0.11%on the Morph Ⅱ dataset.