Abstract. The person re-identification task requires to robustly estimate visual similarities between person images. However, existing person re-identification models mostly estimate the similarities of different
image pairs of probe and gallery images independently while ignores
the relationship information between different probe-gallery pairs. As a
result, the similarity estimation of some hard samples might not be accurate. In this paper, we propose a novel deep learning framework, named
Similarity-Guided Graph Neural Network (SGGNN) to overcome such
limitations. Given a probe image and several gallery images, SGGNN
creates a graph to represent the pairwise relationships between probegallery pairs (nodes) and utilizes such relationships to update the probegallery relation features in an end-to-end manner. Accurate similarity
estimation can be achieved by using such updated probe-gallery relation
features for prediction. The input features for nodes on the graph are the
relation features of different probe-gallery image pairs. The probe-gallery
relation feature updating is then performed by the messages passing in
SGGNN, which takes other nodes’ information into account for similarity
estimation. Different from conventional GNN approaches, SGGNN learns
the edge weights with rich labels of gallery instance pairs directly, which
provides relation fusion more precise information. The effectiveness of
our proposed method is validated on three public person re-identification
datasets