  Learning discriminative image representations plays a vital role in
long-tailed image classification because it can ease the classifier learning in
imbalanced cases. Given the promising performance contrastive learning has
shown recently in representation learning, in this work, we explore effective
supervised contrastive learning strategies and tailor them to learn better
image representations from imbalanced data in order to boost the classification
accuracy thereon. Specifically, we propose a novel hybrid network structure
being composed of a supervised contrastive loss to learn image representations
and a cross-entropy loss to learn classifiers, where the learning is
progressively transited from feature learning to the classifier learning to
embody the idea that better features make better classifiers. We explore two
variants of contrastive loss for feature learning, which vary in the forms but
share a common idea of pulling the samples from the same class together in the
normalized embedding space and pushing the samples from different classes
apart. One of them is the recently proposed supervised contrastive (SC) loss,
which is designed on top of the state-of-the-art unsupervised contrastive loss
by incorporating positive samples from the same class. The other is a
prototypical supervised contrastive (PSC) learning strategy which addresses the
intensive memory consumption in standard SC loss and thus shows more promise
under limited memory budget. Extensive experiments on three long-tailed
classification datasets demonstrate the advantage of the proposed contrastive
learning based hybrid networks in long-tailed classification.
