Reducing Human Annotation Effort Using Self-supervised Learning for Image Segmentation

Image segmentation stands out as one of the most computationally demanding computer vision tasks, posing challenges not only due to the substantial computational resources required for training but also the scarcity of available annotation masks. The creation of a sizable collection of accurate segmentation annotation masks is notorious for being labor-intensive and time-consuming, often acting as a significant bottleneck in image segmentation projects. This paper delves into the intricacies of human effort involved in traditional segmentation annotation and explores the potential impact of self-supervised learning (SSL) as a promising solution. Ultimately, we contend that, despite the tradeoffs inherent in existing SSL approaches for image segmentation, a new alternative leveraging foundation models for image segmentation, capable of zero-shot segmentation across extensive object categories, could emerge as a novel solution that reduces human effort in both annotation and model development.