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.

提出了一种名为 Q-Face 的新型任务自适应多任务人脸分析方法，该方法能够同时执行多个面部分析任务，并在表情识别、动作单元检测、面部属性分析、年龄估计和面部姿态估计等方面达到了最先进的性能。

任务自适应的 Q-Face

Task-adaptive Q-Face

Language models pretrained on text from a wide variety of sources form the
foundation of today's NLP. In light of the success of these broad-coverage
models, we investigate whether it is still helpful to tailor a pretrained model
to the domain of a target task. We present a study across four domains
(biomedical and computer science publications, news, and reviews) and eight
classification tasks, showing that a second phase of pretraining in-domain
(domain-adaptive pretraining) leads to performance gains, under both high- and
low-resource settings. Moreover, adapting to the task's unlabeled data
(task-adaptive pretraining) improves performance even after domain-adaptive
pretraining. Finally, we show that adapting to a task corpus augmented using
simple data selection strategies is an effective alternative, especially when
resources for domain-adaptive pretraining might be unavailable. Overall, we
consistently find that multi-phase adaptive pretraining offers large gains in
task performance.

本研究通过研究不同领域和任务的分类模型，验证了在特定领域和任务上进行二次预训练（领域自适应和任务自适应预训练）可以显著提高性能，同时也发现多阶段适应预训练在任务表现上取得了大幅提升。