  Quantifying and evaluating image complexity can be instrumental in enhancing
the performance of various computer vision tasks. Supervised learning can
effectively learn image complexity features from well-annotated datasets.
However, creating such datasets requires expensive manual annotation costs. The
models may learn human subjective biases from it. In this work, we introduce
the MoCo v2 framework. We utilize contrastive learning to represent image
complexity, named CLIC (Contrastive Learning for Image Complexity). We find
that there are complexity differences between different local regions of an
image, and propose Random Crop and Mix (RCM), which can produce positive
samples consisting of multi-scale local crops. RCM can also expand the train
set and increase data diversity without introducing additional data. We conduct
extensive experiments with CLIC, comparing it with both unsupervised and
supervised methods. The results demonstrate that the performance of CLIC is
comparable to that of state-of-the-art supervised methods. In addition, we
establish the pipelines that can apply CLIC to computer vision tasks to
effectively improve their performance.
