We present a new loss function called Distribution-Balanced Loss for the
multi-label recognition problems that exhibit long-tailed class distributions.
Compared to conventional single-label classification problem, multi-label
recognition problems are often more challenging due to two significant issues,
namely the co-occurrence of labels and the dominance of negative labels (when
treated as multiple binary classification problems). The Distribution-Balanced
Loss tackles these issues through two key modifications to the standard binary
cross-entropy loss: 1) a new way to re-balance the weights that takes into
account the impact caused by label co-occurrence, and 2) a negative tolerant
regularization to mitigate the over-suppression of negative labels. Experiments
on both Pascal VOC and COCO show that the models trained with this new loss
function achieve significant performance gains over existing methods. Code and
models are available at: this https URL .

本文提出了一种称为分布平衡损失函数的新方法，用于解决多标签识别问题中遇到的长尾类分布、标签共现和负标签占主导的问题，并通过实验验证了该方法的有效性。