  Annotating a large-scale image dataset is very tedious, yet necessary for
training person re-identification models. To alleviate such a problem, we
present an active hard sample mining framework via training an effective re-ID
model with the least labeling efforts. Considering that hard samples can
provide informative patterns, we first formulate an uncertainty estimation to
actively select hard samples to iteratively train a re-ID model from scratch.
Then, intra-diversity estimation is designed to reduce the redundant hard
samples by maximizing their diversity. Moreover, we propose a computer-assisted
identity recommendation module embedded in the active hard sample mining
framework to help human annotators to rapidly and accurately label the selected
samples. Extensive experiments were carried out to demonstrate the
effectiveness of our method on several public datasets. Experimental results
indicate that our method can reduce 57%, 63%, and 49% annotation efforts on the
Market1501, MSMT17, and CUHK03, respectively, while maximizing the performance
of the re-ID model.
