  Image retrieval task consists of finding similar images to a query image from
a set of gallery (database) images. Such systems are used in various
applications e.g. person re-identification (ReID) or visual product search.
Despite active development of retrieval models it still remains a challenging
task mainly due to large intra-class variance caused by changes in view angle,
lighting, background clutter or occlusion, while inter-class variance may be
relatively low. A large portion of current research focuses on creating more
robust features and modifying objective functions, usually based on Triplet
Loss. Some works experiment with using centroid/proxy representation of a class
to alleviate problems with computing speed and hard samples mining used with
Triplet Loss. However, these approaches are used for training alone and
discarded during the retrieval stage. In this paper we propose to use the mean
centroid representation both during training and retrieval. Such an aggregated
representation is more robust to outliers and assures more stable features. As
each class is represented by a single embedding - the class centroid - both
retrieval time and storage requirements are reduced significantly. Aggregating
multiple embeddings results in a significant reduction of the search space due
to lowering the number of candidate target vectors, which makes the method
especially suitable for production deployments. Comprehensive experiments
conducted on two ReID and Fashion Retrieval datasets demonstrate effectiveness
of our method, which outperforms the current state-of-the-art. We propose
centroid training and retrieval as a viable method for both Fashion Retrieval
and ReID applications.
