  Real-world visual recognition problems often exhibit long-tailed
distributions, where the amount of data for learning in different categories
shows significant imbalance. Standard classification models learned on such
data distribution often make biased predictions towards the head classes while
generalizing poorly to the tail classes. In this paper, we present two
effective modifications of CNNs to improve network learning from long-tailed
distribution. First, we present a Class Activation Map Calibration (CAMC)
module to improve the learning and prediction of network classifiers, by
enforcing network prediction based on important image regions. The proposed
CAMC module highlights the correlated image regions across data and reinforces
the representations in these areas to obtain a better global representation for
classification. Furthermore, we investigate the use of normalized classifiers
for representation learning in long-tailed problems. Our empirical study
demonstrates that by simply scaling the outputs of the classifier with an
appropriate scalar, we can effectively improve the classification accuracy on
tail classes without losing the accuracy of head classes. We conduct extensive
experiments to validate the effectiveness of our design and we set new
state-of-the-art performance on five benchmarks, including ImageNet-LT,
Places-LT, iNaturalist 2018, CIFAR10-LT, and CIFAR100-LT.
