Crowdsourced Attention

Crowdsourced attention refers to the technique of leveraging the input and focus of a large group of people to identify important or relevant features in data, such as images, text, or videos. By gathering collective insights or annotations from many participants, systems can better understand what aspects are most significant, improving the accuracy and interpretability of machine learning models. This approach decentralizes the process of identifying key information, making it more scalable and robust, especially when expert labeling is costly or impractical. It effectively harnesses collective human intuition to enhance data analysis and model performance.