Although face analysis has achieved remarkable improvements in the past few years, designing a multi-task face analysis model is still challenging. Most face analysis tasks are studied as separate problems and do not benefit from the synergy among related tasks. In this work, we propose a novel task-adaptive multi-task face analysis method named as Q-Face, which simultaneously performs multiple face analysis tasks with a unified model. We fuse the features from multiple layers of a large-scale pre-trained model so that the whole model can use both local and global facial information to support multiple tasks. Furthermore, we design a task-adaptive module that performs cross-attention between a set of query vectors and the fused multi-stage features and finally adaptively extracts desired features for each face analysis task. Extensive experiments show that our method can perform multiple tasks simultaneously and achieves state-of-the-art performance on face expression recognition, action unit detection, face attribute analysis, age estimation, and face pose estimation. Compared to conventional methods, our method opens up new possibilities for multi-task face analysis and shows the potential for both accuracy and efficiency.

翻译：尽管人脸分析在过去几年中取得了显著进展，设计多任务人脸分析模型仍然充满挑战。大多数任务作为独立问题被研究，未能从相关任务间的协同中获益。本文提出一种新颖的任务自适应多任务人脸分析方法Q-Face，该方法通过统一模型同时执行多种人脸分析任务。我们融合大规模预训练模型多层级特征，使整体模型能同时利用局部与全局人脸信息支撑多项任务。进一步地，我们设计了一个任务自适应模块，该模块在一组查询向量与融合的多阶段特征之间执行交叉注意力机制，最终为人脸分析任务自适应提取所需特征。大量实验表明，我们的方法能同时执行多项任务，并在人脸表情识别、动作单元检测、人脸属性分析、年龄估计及人脸姿态估计上取得最先进性能。相比传统方法，本方法为多任务人脸分析开辟了新可能，并展现出在准确性与效率方面的潜力。