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BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition
[article]
2020
arXiv
pre-print
Our work focuses on tackling the challenging but natural visual recognition task of long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples). In the literature, class re-balancing strategies (e.g., re-weighting and re-sampling) are the prominent and effective methods proposed to alleviate the extreme imbalance for dealing with long-tailed problems. In this paper, we firstly discover that these re-balancing methods achieving
arXiv:1912.02413v4
fatcat:pfruiytwivanjlrer6daf4eede