  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/

Computer Vision and Pattern Recognition;Machine Learning