  In person re-identification (ReID) tasks, many works explore the learning of
part features to improve the performance over global image features. Existing
methods explicitly extract part features by either using a hand-designed image
division or keypoints obtained with external visual systems. In this work, we
propose to learn Discriminative implicit Parts (DiPs) which are decoupled from
explicit body parts. Therefore, DiPs can learn to extract any discriminative
features that can benefit in distinguishing identities, which is beyond
predefined body parts (such as accessories). Moreover, we propose a novel
implicit position to give a geometric interpretation for each DiP. The implicit
position can also serve as a learning signal to encourage DiPs to be more
position-equivariant with the identity in the image. Lastly, an additional DiP
weighting is introduced to handle the invisible or occluded situation and
further improve the feature representation of DiPs. Extensive experiments show
that the proposed method achieves state-of-the-art performance on multiple
person ReID benchmarks.
