**Title: AI Models for Identifying Alcoholic Beverages in Images**
**Introduction:**
Alcohol is a major health concern, accounting for 5.1% of the global disease burden and negatively impacting individuals and the economy. With alcohol exposure prevalent in various forms of media, researchers have been exploring ways to measure and analyze alcohol exposure. One approach is using deep learning models like the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA). However, these models require a large amount of annotated data for training. An alternative approach is Zero-Shot Learning (ZSL) using the Contrastive Language-Image Pretraining (CLIP) model.
**ZSL vs. ABIDLA2:**
Researchers compared the performance of a ZSL model with a deep learning algorithm called ABIDLA2, specifically trained to identify alcoholic beverages in images. The evaluation involved various metrics, such as unweighted average recall (UAR), F1 score, and per-class recall, for both named and descriptive phrases. The test dataset used in the evaluation contained eight beverage categories with a uniform distribution for accurate evaluation.
**Findings:**
The researchers discovered that ZSL performed well in some tasks but struggled with fine-grained classification. ABIDLA2 outperformed ZSL in identifying specific beverage categories. However, ZSL using descriptive phrases (e.g., “this is a picture of someone holding a beer bottle”) performed nearly as well as ABIDLA2 in classifying beverages into broader categories (e.g., beer, wine, spirits). Interestingly, ZSL surpassed ABIDLA2 when determining whether a picture included alcohol content or not.
**Importance of Phrase Engineering:**
The researchers identified the significance of phrase engineering for ZSL to achieve higher performance, especially for the ‘others’ class.
**Advantages of ZSL:**
ZSL requires minimal additional training data and computational resources, making it a more practical option compared to supervised learning algorithms like ABIDLA2. It can accurately identify alcohol content in images, particularly for binary classification tasks. This research encourages future studies to compare the generalization capability of supervised learning models to ZSL using real-life datasets that encompass diverse populations and cultures.
**Conclusion:**
Improved methods for identifying alcoholic beverages in images using AI models like ZSL are being explored. These models offer a more efficient and accessible approach compared to traditional supervised learning algorithms. Further research in this area will contribute to our understanding of alcohol exposure and its impact on society.
**Credit:**
This research was conducted by a group of researchers. To learn more, refer to the paper (https://www.marktechpost.com/2023/07/27/detecting-alcohol-exposure-in-media-evaluating-the-power-of-clips-zero-shot-learning-vs-abidla2-deep-learning-in-image-analysis/(https://www.nature.com/articles/s41598-023-39169-4)).