  Person Re-identification (Person ReID) is an important topic in intelligent
surveillance and computer vision. It aims to accurately measure visual
similarities between person images for determining whether two images
correspond to the same person. The key to accurately measure visual
similarities is learning discriminative features, which not only captures clues
from different spatial scales, but also jointly inferences on multiple scales,
with the ability to determine reliability and ID-relativity of each clue. To
achieve these goals, we propose to improve Person ReID system performance from
two perspective: \textbf{1).} Multi-scale feature learning (MSFL), which
consists of Cross-scale information propagation (CSIP) and Multi-scale feature
fusion (MSFF), to dynamically fuse features cross different scales.\textbf{2).}
Multi-scale gradient regularizor (MSGR), to emphasize ID-related factors and
ignore irrelevant factors in an adversarial manner. Combining MSFL and MSGR,
our method achieves the state-of-the-art performance on four commonly used
person-ReID datasets with neglectable test-time computation overhead.
