  Person re-identification is a challenging task due to various complex
factors. Recent studies have attempted to integrate human parsing results or
externally defined attributes to help capture human parts or important object
regions. On the other hand, there still exist many useful contextual cues that
do not fall into the scope of predefined human parts or attributes. In this
paper, we address the missed contextual cues by exploiting both the accurate
human parts and the coarse non-human parts. In our implementation, we apply a
human parsing model to extract the binary human part masks \emph{and} a
self-attention mechanism to capture the soft latent (non-human) part masks. We
verify the effectiveness of our approach with new state-of-the-art performances
on three challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03. Our
implementation is available at https://github.com/ggjy/P2Net.pytorch.
