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Current training objectives of existing person Re-IDentification (ReID)models only ensure that the loss of the model decreases on selected trainingbatch, with no regards to the performance on samples outside the batch. It willinevitably cause the model to over-fit the data in the dominant position (e.g.,head data in imbalanced class, easy samples or noisy samples). %We call thesample that updates the model towards generalizing on more data a generalizablesample. The latest resampling methods address the issue by designing specificcriterion to select specific samples that trains the model generalize more oncertain type of data (e.g., hard samples, tail data), which is not adaptive tothe inconsistent real world ReID data distributions. Therefore, instead ofsimply presuming on what samples are generalizable, this paper proposes aone-for-more training objective that directly takes the generalization abilityof selected samples as a loss function and learn a sampler to automaticallyselect generalizable samples. More importantly, our proposed one-for-more basedsampler can be seamlessly integrated into the ReID training framework which isable to simultaneously train ReID models and the sampler in an end-to-endfashion. The experimental results show that our method can effectively improvethe ReID model training and boost the performance of ReID models.

One for More: Selecting Generalizable Samples for Generalizable ReID Model