How to analyze visual data using zero-shot learning: An overview and tutorial.

Area of Science:

• Psychology
• Computer Science
• Data Science

Background:

• The proliferation of smartphone cameras and social media generates vast amounts of visual data daily.
• Analyzing this image data offers psychological insights but traditional methods are time-intensive or require technical expertise.
• Zero-shot learning presents a less technically demanding alternative for researchers.

Purpose of the Study:

• To provide a tutorial and guide for psychology researchers on analyzing visual data using zero-shot learning.
• To demonstrate the application of two popular zero-shot learning models: Contrastive Language-Image Pretraining (CLIP) and Large Language and Vision Assistant (LLVA).
• To offer practical guidance on interpreting results, creating validation datasets, and implementing models for new data.

Main Methods:

• Utilized two pretrained zero-shot learning models (CLIP and LLVA) to identify beverages in a manipulated image dataset.
• The dataset varied beverage type, setting, and prominence (foreground, midground, background).
• Provided open-source code and data via GitHub and Google Colab for reproducibility.

Main Results:

• Demonstrated the feasibility of using zero-shot learning models for image analysis in a psychological research context.
• Successfully identified beverages within the dataset, showcasing the models' capabilities.
• Detailed steps for implementation, interpretation, and validation were provided.

Conclusions:

• Zero-shot learning significantly lowers the technical barrier for psychology researchers to analyze visual data.
• This approach facilitates deeper insights from the growing volume of image data.
• The tutorial aims to empower researchers to adopt advanced AI techniques for their studies.