  Full attention, which generates an attention value per element of the input
feature maps, has been successfully demonstrated to be beneficial in visual
tasks. In this work, we propose a fully attentional network, termed {\it
channel recurrent attention network}, for the task of video pedestrian
retrieval. The main attention unit, \textit{channel recurrent attention},
identifies attention maps at the frame level by jointly leveraging spatial and
channel patterns via a recurrent neural network. This channel recurrent
attention is designed to build a global receptive field by recurrently
receiving and learning the spatial vectors. Then, a \textit{set aggregation}
cell is employed to generate a compact video representation. Empirical
experimental results demonstrate the superior performance of the proposed deep
network, outperforming current state-of-the-art results across standard video
person retrieval benchmarks, and a thorough ablation study shows the
effectiveness of the proposed units.
