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Recent studies in image retrieval task have shown that ensembling differentmodels and combining multiple global descriptors lead to performanceimprovement. However, training different models for the ensemble is not onlydifficult but also inefficient with respect to time and memory. In this paper,we propose a novel framework that exploits multiple global descriptors to getan ensemble effect while it can be trained in an end-to-end manner. Theproposed framework is flexible and expandable by the global descriptor, CNNbackbone, loss, and dataset. Moreover, we investigate the effectiveness ofcombining multiple global descriptors with quantitative and qualitativeanalysis. Our extensive experiments show that the combined descriptoroutperforms a single global descriptor, as it can utilize different types offeature properties. In the benchmark evaluation, the proposed frameworkachieves the state-of-the-art performance on the CARS196, CUB200-2011, In-shopClothes, and Stanford Online Products on image retrieval tasks. Our modelimplementations and pretrained models are publicly available.

Combination of Multiple Global Descriptors for Image Retrieval