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Image retrieval can be formulated as a ranking problem where the goal is toorder database images by decreasing similarity to the query. Recent deep modelsfor image retrieval have outperformed traditional methods by leveragingranking-tailored loss functions, but important theoretical and practicalproblems remain. First, rather than directly optimizing the global ranking,they minimize an upper-bound on the essential loss, which does not necessarilyresult in an optimal mean average precision (mAP). Second, these methodsrequire significant engineering efforts to work well, e.g. special pre-trainingand hard-negative mining. In this paper we propose instead to directly optimizethe global mAP by leveraging recent advances in listwise loss formulations.Using a histogram binning approximation, the AP can be differentiated and thusemployed to end-to-end learning. Compared to existing losses, the proposedmethod considers thousands of images simultaneously at each iteration andeliminates the need for ad hoc tricks. It also establishes a new state of theart on many standard retrieval benchmarks. Models and evaluation scripts havebeen made available at https://europe.naverlabs.com/Deep-Image-Retrieval/

Learning with Average Precision: Training Image Retrieval with a Listwise Loss