论文标题
通过共享行人属性识别的共享多任务学习
Multi-Task Learning via Co-Attentive Sharing for Pedestrian Attribute Recognition
论文作者
论文摘要
学习预测行人的多个属性是一个多任务学习问题。为了在两个单独的任务网络之间共享特征表示，诸如交叉缝线和sluice网络之类的传统方法学习功能或特征子空间的线性组合。但是，线性组合排除了通道之间的复杂相互依赖性。此外，空间信息交换的考虑较少。在本文中，我们提出了一种新颖的共同共享（CAS）模块，该模块提取歧视通道和空间区域，以在多任务学习中更有效的特征共享。该模块由三个分支组成，该分支分别利用不同的通道进行功能融合，注意力集和特定于任务的功能增强。在两个行人属性识别数据集上的实验表明，与使用许多指标的最新方法相比，我们的模块的表现优于常规共享单元，并取得了优越的结果。
Learning to predict multiple attributes of a pedestrian is a multi-task learning problem. To share feature representation between two individual task networks, conventional methods like Cross-Stitch and Sluice network learn a linear combination of features or feature subspaces. However, linear combination rules out the complex interdependency between channels. Moreover, spatial information exchanging is less-considered. In this paper, we propose a novel Co-Attentive Sharing (CAS) module which extracts discriminative channels and spatial regions for more effective feature sharing in multi-task learning. The module consists of three branches, which leverage different channels for between-task feature fusing, attention generation and task-specific feature enhancing, respectively. Experiments on two pedestrian attribute recognition datasets show that our module outperforms the conventional sharing units and achieves superior results compared to the state-of-the-art approaches using many metrics.
