Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation

Scene Graph Generation (SGG) aims to represent visual scenes by identifying objects and their pairwise relationships, providing a structured understanding of image content. However, inherent challenges like long-tailed class distributions and prediction variability necessitate uncertainty quantification in SGG for its practical viability. In this paper, we introduce a novel Conformal Prediction based framework, adaptive to any existing SGG method, for quantifying their predictive uncertainty by constructing well-calibrated prediction sets over their generated scene graphs. These scene graph prediction sets are designed to achieve statistically rigorous coverage guarantees under exchangeability assumptions. Additionally, to ensure the prediction sets contain the most practically interpretable scene graphs, we propose an effective MLLM-based postprocessing strategy for selecting the most visually and semantically plausible scene graphs within each set. We show that our proposed approach can produce diverse possible scene graphs from an image, assess the reliability of SGG methods, and improve overall SGG performance.