  Person re-identification (reID) by CNNs based networks has achieved favorable
performance in recent years. However, most of existing CNNs based methods do
not take full advantage of spatial-temporal context modeling. In fact, the
global spatial-temporal context can greatly clarify local distractions to
enhance the target feature representation. To comprehensively leverage the
spatial-temporal context information, in this work, we present a novel block,
Interaction-Aggregation-Update (IAU), for high-performance person reID.
Firstly, Spatial-Temporal IAU (STIAU) module is introduced. STIAU jointly
incorporates two types of contextual interactions into a CNN framework for
target feature learning. Here the spatial interactions learn to compute the
contextual dependencies between different body parts of a single frame. While
the temporal interactions are used to capture the contextual dependencies
between the same body parts across all frames. Furthermore, a Channel IAU
(CIAU) module is designed to model the semantic contextual interactions between
channel features to enhance the feature representation, especially for
small-scale visual cues and body parts. Therefore, the IAU block enables the
feature to incorporate the globally spatial, temporal, and channel context. It
is lightweight, end-to-end trainable, and can be easily plugged into existing
CNNs to form IAUnet. The experiments show that IAUnet performs favorably
against state-of-the-art on both image and video reID tasks and achieves
compelling results on a general object categorization task. The source code is
available at https://github.com/blue-blue272/ImgReID-IAnet.
