Contextual Multi-Scale Feature Learning for Person Re-Identification

Representing features at multiple scales is significant for person reidentification (Re-ID). Most existing methods learn the multi-scale features by stacking streams and convolutions without considering the cooperation of multiple scales at a granular level. However, most scales are more discriminative only when they integrate other scales as contextual information. We termed that contextual multi-scale. In this paper, we proposed a novel architecture, namely contextual multi-scale network (CMSNet), for learning common and contextual multi-scale representations simultaneously. The building block of CMSNet obtains contextual multi-scale representations by bidirectionally hierarchical connection groups: the forward hierarchical connection group for stepwise interscale information fusion and the backward hierarchical connection group for leap-frogging inter-scale information fusion. Too rich scale features without a selection will confuse the discrimination. Additionally, we introduced a new channel-wise scale selection module to dynamically select scale features for corresponding input image. To the best of our knowledge, CMSNet is the most lightweight model for person Re-ID and it achieves state-of-the-art performance on four commonly used Re-ID datasets, surpassing most large-scale models.