Contrastive Learning for Image Complexity Representation
This addresses the need for cost-effective and unbiased image complexity representation in computer vision, though it is incremental as it builds on existing contrastive learning frameworks.
The paper tackles the problem of quantifying image complexity without expensive manual annotations by introducing CLIC, a contrastive learning framework that uses Random Crop and Mix to generate multi-scale local crops, achieving performance comparable to state-of-the-art supervised methods.
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.