  Long-tailed datasets are very frequently encountered in real-world use cases
where few classes or categories (known as majority or head classes) have higher
number of data samples compared to the other classes (known as minority or tail
classes). Training deep neural networks on such datasets gives results biased
towards the head classes. So far, researchers have come up with multiple
weighted loss and data re-sampling techniques in efforts to reduce the bias.
However, most of such techniques assume that the tail classes are always the
most difficult classes to learn and therefore need more weightage or attention.
Here, we argue that the assumption might not always hold true. Therefore, we
propose a novel approach to dynamically measure the instantaneous difficulty of
each class during the training phase of the model. Further, we use the
difficulty measures of each class to design a novel weighted loss technique
called `class-wise difficulty based weighted (CDB-W) loss' and a novel data
sampling technique called `class-wise difficulty based sampling (CDB-S)'. To
verify the wide-scale usability of our CDB methods, we conducted extensive
experiments on multiple tasks such as image classification, object detection,
instance segmentation and video-action classification. Results verified that
CDB-W loss and CDB-S could achieve state-of-the-art results on many
class-imbalanced datasets such as ImageNet-LT, LVIS and EGTEA, that resemble
real-world use cases.
