Rethinking Person Re-identification from a Projection-on-Prototypes
  Perspective
Qizao Wang Xuelin Qian Bin Li Yanwei Fu Xiangyang Xue
Person Re-IDentification (Re-ID) as a retrieval task, has achieved tremendous
development over the past decade. Existing state-of-the-art methods follow an
analogous framework to first extract features from the input images and then
categorize them with a classifier. However, since there is no identity overlap
between training and testing sets, the classifier is often discarded during
inference. Only the extracted features are used for person retrieval via
distance metrics. In this paper, we rethink the role of the classifier in
person Re-ID, and advocate a new perspective to conceive the classifier as a
projection from image features to class prototypes. These prototypes are
exactly the learned parameters of the classifier. In this light, we describe
the identity of input images as similarities to all prototypes, which are then
utilized as more discriminative features to perform person Re-ID. We thereby
propose a new baseline ProNet, which innovatively reserves the function of the
classifier at the inference stage. To facilitate the learning of class
prototypes, both triplet loss and identity classification loss are applied to
features that undergo the projection by the classifier. An improved version of
ProNet++ is presented by further incorporating multi-granularity designs.
Experiments on four benchmarks demonstrate that our proposed ProNet is simple
yet effective, and significantly beats previous baselines. ProNet++ also
achieves competitive or even better results than transformer-based competitors.