  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.
