  The calibration for deep neural networks is currently receiving widespread
attention and research. Miscalibration usually leads to overconfidence of the
model. While, under the condition of long-tailed distribution of data, the
problem of miscalibration is more prominent due to the different confidence
levels of samples in minority and majority categories, and it will result in
more serious overconfidence. To address this problem, some current research
have designed diverse temperature coefficients for different categories based
on temperature scaling (TS) method. However, in the case of rare samples in
minority classes, the temperature coefficient is not generalizable, and there
is a large difference between the temperature coefficients of the training set
and the validation set. To solve this challenge, this paper proposes a
dual-branch temperature scaling calibration model (Dual-TS), which considers
the diversities in temperature parameters of different categories and the
non-generalizability of temperature parameters for rare samples in minority
classes simultaneously. Moreover, we noticed that the traditional calibration
evaluation metric, Excepted Calibration Error (ECE), gives a higher weight to
low-confidence samples in the minority classes, which leads to inaccurate
evaluation of model calibration. Therefore, we also propose Equal Sample Bin
Excepted Calibration Error (Esbin-ECE) as a new calibration evaluation metric.
Through experiments, we demonstrate that our model yields state-of-the-art in
both traditional ECE and Esbin-ECE metrics.
