To enhance the generalization performance of Multi-Task Networks (MTN) in Face Attribute Recognition (FAR), it is crucial to share relevant information across multiple related prediction tasks effectively. Traditional MTN methods create shared low-level modules and distinct high-level modules, causing an exponential increase in model parameters with the addition of tasks. This approach also limits feature interaction at the high level, hindering the exploration of semantic relations among attributes, thereby affecting generalization negatively. In response, this study introduces FAR-AMTN, a novel Attention Multi-Task Network for FAR. It incorporates a Weight-Shared Group-Specific Attention (WSGSA) module with shared parameters to minimize complexity while improving group feature representation. Furthermore, a Cross-Group Feature Fusion (CGFF) module is utilized to foster interactions between attribute groups, enhancing feature learning. A Dynamic Weighting Strategy (DWS) is also introduced for synchronized task convergence. Experiments on the CelebA and LFWA datasets demonstrate that the proposed FAR-AMTN demonstrates superior accuracy with significantly fewer parameters compared to existing models.

翻译：为提升多任务网络（MTN）在人脸属性识别（FAR）中的泛化性能，有效共享多个相关预测任务间的相关信息至关重要。传统的MTN方法构建共享的低层模块和独立的高层模块，导致模型参数随任务增加呈指数级增长。该方法同时限制了高层特征交互，阻碍了属性间语义关系的探索，从而对泛化性能产生负面影响。为此，本研究提出FAR-AMTN，一种用于FAR的新型注意力多任务网络。它引入了一个具有共享参数的权重共享-组特定注意力（WSGSA）模块，以在提升组特征表示能力的同时最小化模型复杂度。此外，采用跨组特征融合（CGFF）模块促进属性组间的交互，从而增强特征学习。本文还引入了动态权重策略（DWS）以实现任务的同步收敛。在CelebA和LFWA数据集上的实验表明，与现有模型相比，所提出的FAR-AMTN在参数显著减少的同时展现出更优的识别准确率。