  The long-tailed distribution is a common phenomenon in the real world.
Extracted large scale image datasets inevitably demonstrate the long-tailed
property and models trained with imbalanced data can obtain high performance
for the over-represented categories, but struggle for the under-represented
categories, leading to biased predictions and performance degradation. To
address this challenge, we propose a novel de-biasing method named Inverse
Image Frequency (IIF). IIF is a multiplicative margin adjustment transformation
of the logits in the classification layer of a convolutional neural network.
Our method achieves stronger performance than similar works and it is
especially useful for downstream tasks such as long-tailed instance
segmentation as it produces fewer false positive detections. Our extensive
experiments show that IIF surpasses the state of the art on many long-tailed
benchmarks such as ImageNet-LT, CIFAR-LT, Places-LT and LVIS, reaching 55.8%
top-1 accuracy with ResNet50 on ImageNet-LT and 26.2% segmentation AP with
MaskRCNN on LVIS. Code available at https://github.com/kostas1515/iif
