Self-Supervised Regression for Query Performance Prediction on Image Retrieval

Content-Based Image Retrieval (CBIR) systems are currently a widely used solution for image retrieval tasks with various applications. Despite the advances achieved, one of the central issues is the need for methods capable of handling the scarcity or absence of labeled data. In this scenario, Query Performance Prediction (QPP) approaches represent a successful technique in the effectiveness estimation of retrieval results. In this work, we propose a novel self-supervised framework, named Regression for Query Performance Prediction Framework - RQPPF, which is flexible and can be used with different regression models. Among the contributions, our training relies only on synthetic data and rank-based features. An experimental evaluation was conducted on 4 different retrieval datasets, considering 14 visual features and 11 regression models. The results indicate highly effective predictions and most of them are greater than recent baselines.