2 research outputs found
Rethinking Person Re-identification from a Projection-on-Prototypes Perspective
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
Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging
task, suffering from two limitations of inferior identity-relevant features and
limited training samples. Existing methods mainly leverage auxiliary
information to facilitate discriminative feature learning, including
soft-biometrics features of shapes and gaits, and additional labels of
clothing. However, these information may be unavailable in real-world
applications. In this paper, we propose a novel FIne-grained Representation and
Recomposition (FIRe) framework to tackle both limitations without any
auxiliary information. Specifically, we first design a Fine-grained Feature
Mining (FFM) module to separately cluster images of each person. Images with
similar so-called fine-grained attributes (e.g., clothes and viewpoints) are
encouraged to cluster together. An attribute-aware classification loss is
introduced to perform fine-grained learning based on cluster labels, which are
not shared among different people, promoting the model to learn
identity-relevant features. Furthermore, by taking full advantage of the
clustered fine-grained attributes, we present a Fine-grained Attribute
Recomposition (FAR) module to recompose image features with different
attributes in the latent space. It can significantly enhance representations
for robust feature learning. Extensive experiments demonstrate that FIRe
can achieve state-of-the-art performance on five widely-used cloth-changing
person Re-ID benchmarks