Rapid Image Labeling via Neuro-Symbolic Learning
This addresses the challenge of data labeling in specialized domains where expertise is required, offering a solution for reducing annotation costs.
The paper tackles the problem of expensive manual image annotation in domains like healthcare by proposing Rapid, a neuro-symbolic approach that infers labeling rules from small labeled datasets, achieving 83.33% to 88.33% accuracy on four tasks with only 12 to 39 samples.
The success of Computer Vision (CV) relies heavily on manually annotated data. However, it is prohibitively expensive to annotate images in key domains such as healthcare, where data labeling requires significant domain expertise and cannot be easily delegated to crowd workers. To address this challenge, we propose a neuro-symbolic approach called Rapid, which infers image labeling rules from a small amount of labeled data provided by domain experts and automatically labels unannotated data using the rules. Specifically, Rapid combines pre-trained CV models and inductive logic learning to infer the logic-based labeling rules. Rapid achieves a labeling accuracy of 83.33% to 88.33% on four image labeling tasks with only 12 to 39 labeled samples. In particular, Rapid significantly outperforms finetuned CV models in two highly specialized tasks. These results demonstrate the effectiveness of Rapid in learning from small data and its capability to generalize among different tasks. Code and our dataset are publicly available at https://github.com/Neural-Symbolic-Image-Labeling/