SCALE-INVARIANT SIAMESE NETWORK FOR PERSON RE-IDENTIFICATION

Most existing methods for person re-identification (ReID) almost match people at a single scale and ignore that people are often distinguishable at the right spatial locations and scales. Unlike previous works designing complex convolutional neural network (CNN) architecture or concatenating multi-branch scale-specific features, we aim to employ a simple network to learn scale-invariant features. Concretely, we first propose a shared two-branch framework with two-scale images from the same identity as inputs, which is beneficial for ReID network to focus on common features in different-scale images. Furthermore, we introduce a novel attention loss to enforce discriminative regions between two branches more consistent in the visual level. Finally, we conduct extensive evaluations on three large-scale datasets and report competitive performance.