Alcohol, a prevalent health concern, represents 5.1% of the worldwide burden of disease, causing a major negative impact on individuals and the economy. From social media to movies, promoting, and popular music, alcohol exposure is in all places. Researchers suggest a link between exposure to alcohol-related social media posts and alcohol use, particularly amongst young adults. The researchers are exploring modern approaches to measure and analyze alcohol exposure. Supervised deep learning models like Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA) have shown promise in identifying alcoholic beverages from images but require an enormous amount of manually annotated data for training. 

An alternating approach to that is Zero-Shot Learning (ZSL) utilizing the Contrastive Language-Image Pretraining (CLIP) model. The researchers have investigated the performance of a ZSL model in comparison with a deep learning algorithm specifically trained to discover alcoholic beverages in images (ABIDLA2). The test dataset utilized by research scholars for evaluation is utilized in the ABIDLA2 paper, ABD22, containing eight beverage categories. The testing set has 1762 per class to keep up a uniform distribution for evaluation. The evaluation involves three tasks, and the performance metrics, equivalent to unweighted average recall (UAR), F1 rating, and per-class recall, were computed and compared for ABIDLA2 and ZSL for each named and descriptive phrases. 

The researchers found that ZSL performed well in some tasks but needed help with fine-grained classification. The ABIDLA2 model outperformed ZSL in identifying specific beverage categories. Nonetheless, ZSL using descriptive phrases (e.g., “this can be a picture of somebody holding a beer bottle”) performed nearly in addition to ABIDLA2 on classifying specific beverages into broader beverage categories (beer, wine, spirits, and others, i.e., Task 2) and even surpassed ABIDLA2 when classifying whether an image included alcohol content or not. 

They identified that phrase engineering is crucial for ZSL to realize higher performance, especially for the ‘others’ class. 

One in all the important thing strengths of this work is that ZSL requires minimal additional training data and computational resources and fewer computer science expertise in comparison with the supervised learning algorithm. It could accurately address research questions equivalent to identifying alcohol content in images, especially when binary classification is required. The findings encourage future works to match the generalization capability of supervised learning models to ZSL on real-life datasets that include images of various populations and cultures.

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