  Person Re-identification (ReID) has achieved significant improvement due to
the adoption of Convolutional Neural Networks (CNNs). However, person ReID
systems only provide a distance or similarity when matching two persons, which
makes users hardly understand why they are similar or not. Therefore, we
propose an Attribute-guided Metric Interpreter, named AttriMeter, to
semantically and quantitatively explain the results of CNN-based ReID models.
The AttriMeter has a pluggable structure that can be grafted on arbitrary
target models, i.e., the ReID models that need to be interpreted. With an
attribute decomposition head, it can learn to generate a group of
attribute-guided attention maps (AAMs) from the target model. By applying AAMs
to features of two persons from the target model, their distance will be
decomposed into a set of attribute-guided components that can measure the
contributions of individual attributes. Moreover, we design a distance
distillation loss to guarantee the consistency between the results from the
target model and the decomposed components from AttriMeter, and an attribute
prior loss to eliminate the biases caused by the unbalanced distribution of
attributes. Finally, extensive experiments and analysis on a variety of ReID
models and datasets show the effectiveness of AttriMeter.
