A Survey of Data Optimization for Problems in Computer Vision Datasets

Recent years have witnessed remarkable progress in artificial intelligence (AI) thanks to refined deep network structures, powerful computing devices, and large-scale labeled datasets. However, researchers have mainly invested in the optimization of models and computational devices, leading to the fact that good models and powerful computing devices are currently readily available, while datasets are still stuck at the initial stage of large-scale but low quality. Data becomes a major obstacle to AI development. Taking note of this, we dig deeper and find that there has been some but unstructured work on data optimization. They focus on various problems in datasets and attempt to improve dataset quality by optimizing its structure to facilitate AI development. In this paper, we present the first review of recent advances in this area. First, we summarize and analyze various problems that exist in large-scale computer vision datasets. We then define data optimization and classify data optimization algorithms into three directions according to the optimization form: data sampling, data subset selection, and active learning. Next, we organize these data optimization works according to data problems addressed, and provide a systematic and comparative description. Finally, we summarize the existing literature and propose some potential future research topics.