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Active learning promises to improve annotation efficiency by iterativelyselecting the most important data to be annotated first. However, we uncover astriking contradiction to this promise: active learning fails to select data asefficiently as random selection at the first few choices. We identify this asthe cold start problem in vision active learning, caused by a biased andoutlier initial query. This paper seeks to address the cold start problem byexploiting the three advantages of contrastive learning: (1) no annotation isrequired; (2) label diversity is ensured by pseudo-labels to mitigate bias; (3)typical data is determined by contrastive features to reduce outliers.Experiments are conducted on CIFAR-10-LT and three medical imaging datasets(i.e. Colon Pathology, Abdominal CT, and Blood Cell Microscope). Our initialquery not only significantly outperforms existing active querying strategiesbut also surpasses random selection by a large margin. We foresee our solutionto the cold start problem as a simple yet strong baseline to choose the initialquery for vision active learning. Code is available:https://github.com/c-liangyu/CSVAL

Making Your First Choice: To Address Cold Start Problem in Vision Active Learning