A multimodal deep learning architecture for smoking detection with a
  small data approach
Robert Lakatos Peter Pollner Andras Hajdu Tamas Joo
Introduction: Covert tobacco advertisements often raise regulatory measures.
This paper presents that artificial intelligence, particularly deep learning,
has great potential for detecting hidden advertising and allows unbiased,
reproducible, and fair quantification of tobacco-related media content.
Methods: We propose an integrated text and image processing model based on deep
learning, generative methods, and human reinforcement, which can detect smoking
cases in both textual and visual formats, even with little available training
data. Results: Our model can achieve 74\% accuracy for images and 98\% for
text. Furthermore, our system integrates the possibility of expert intervention
in the form of human reinforcement. Conclusions: Using the pre-trained
multimodal, image, and text processing models available through deep learning
makes it possible to detect smoking in different media even with few training
data.