Fig. 2.

The impact of long-tail data distribution on classification and the proposed solution for this study. A For the tail sample (yellow dots), it is difficult for the model to learn the valid classification when using classic binary cross-entropy loss. B $L_{\mathit{WBCE}}$ increases the weight of tail samples and reduces the weight of head samples (blue dots) through reweighting, which can effectively enhance the learning of tailed categories. C Our design loss ($L_{\mathit{ours}}$) simultaneously increases the weight of tailed data and reduces the contribution of easy head samples, which may help to improve the classification ability of the model when training on a long-tail dataset