  Recent studies in image retrieval task have shown that ensembling different
models and combining multiple global descriptors lead to performance
improvement. However, training different models for the ensemble is not only
difficult but also inefficient with respect to time and memory. In this paper,
we propose a novel framework that exploits multiple global descriptors to get
an ensemble effect while it can be trained in an end-to-end manner. The
proposed framework is flexible and expandable by the global descriptor, CNN
backbone, loss, and dataset. Moreover, we investigate the effectiveness of
combining multiple global descriptors with quantitative and qualitative
analysis. Our extensive experiments show that the combined descriptor
outperforms a single global descriptor, as it can utilize different types of
feature properties. In the benchmark evaluation, the proposed framework
achieves the state-of-the-art performance on the CARS196, CUB200-2011, In-shop
Clothes, and Stanford Online Products on image retrieval tasks. Our model
implementations and pretrained models are publicly available.
