  Person re-identification (reID) benefits greatly from deep convolutional
neural networks (CNNs) which learn robust feature embeddings. However, CNNs are
inherently limited in modeling the large variations in person pose and scale
due to their fixed geometric structures. In this paper, we propose a novel
network structure, Interaction-and-Aggregation (IA), to enhance the feature
representation capability of CNNs. Firstly, Spatial IA (SIA) module is
introduced. It models the interdependencies between spatial features and then
aggregates the correlated features corresponding to the same body parts. Unlike
CNNs which extract features from fixed rectangle regions, SIA can adaptively
determine the receptive fields according to the input person pose and scale.
Secondly, we introduce Channel IA (CIA) module which selectively aggregates
channel features to enhance the feature representation, especially for
smallscale visual cues. Further, IA network can be constructed by inserting IA
blocks into CNNs at any depth. We validate the effectiveness of our model for
person reID by demonstrating its superiority over state-of-the-art methods on
three benchmark datasets.
