For long-tailed image recognition tasks, re-weighting is effective to alleviate data imbalance by assigning higher weights to tail categories. However, existing re-weighting methods typically adopt a static weighting scheme, which usually hurts the accuracy of head categories. To deal with this issue, this paper proposes a progress-relevant weighting scheme called dynamic re-weighting, in which the weight assigned to a particular category first increases and then decreases, proportional to the number of samples that have been used in that category. In addition, we introduce a head-to-tail loss to control the evolving of weights, which makes the model gradually transfer its attention from head categories to tail categories. We conduct experiments on long-tailed CIFAR/ImageNet datasets, and confirm that our method not only outperforms static re-weighting methods, but also improves the accuracy on tail categories without sacrificing the accuracy of head categories.