Tackling class imbalance in computer vision: a contemporary review

Class imbalance is a key issue affecting the performance of computer vision applications such as medical image analysis, objection detection and recognition, image segmentation, scene understanding, and many others. Class imbalance refers to the situation when the number of samples in the majority classes outnumber the minority class populations. The model might then get biased towards the majority classes while neglecting the minority classes, adversely affecting the classification performance. In this paper, an extensive literature survey has been conducted to discuss in depth about the class imbalance issues affecting various classification tasks in computer vision. The study analyzes the performance of several contemporary machine learning algorithms such as chi-square support vector machine and gradient boosted decision trees, and deep learning models such as deep pre-trained convolutional networks, generative adversarial networks and vision transformers, for effective learning from imbalanced computer vision datasets. Most of these models either perform data-level manipulation (data augmentation) or cost-sensitive learning (loss functions) or a combination of the two. This survey also includes a summary of novel deep learning frameworks customized to mitigate the effect of class imbalance. It has included recent advancement and new developments in this field such as Explainable AI. The scrutiny of various popular and benchmark imbalanced datasets in computer vision and performance evaluation metrics are also included as a part of this study. Along with that it has emphasized on the research gaps in contemporary literature which would contribute towards future artificial vision models that can learn effectively from imbalanced datasets.