Probabilistic Inference under Probabilistic Graphical Models for Multi-Modal Data
Year: 2026
Type: preprint
Abstract: This paper presents a novel approach to multi-modal data inference utilizing probabilistic graphical models. The core challenge in processing multi-modal data, such as images, text, and audio, lies in effectively integrating information from different sources and performing robust inference. Existing methods often struggle with the inherent complexities arising from varying data types, correlations, and potential ambiguities. This work proposes a hybrid probabilistic graphical model framework designed to address these limitations. The framework incorporates multiple probabilistic graph models—including Bayesian Networks and Markov Networks—within a unified structure. This allows for the explicit representation and reasoning about relationships between different modalities. The key innovation lies in the design of an effective fusion mechanism that leverages the interconnectedness of the diverse probabilistic components. The proposed model facilitates cross-modal inference and knowledge integration, offering a more nuanced and accurate understanding of multi-modal data. We demonstrate the core claim by outlining a design that can effectively handle multi-modal data, leading to improved reasoning and knowledge combination. The core mechanism involves constructing a mixed model with multiple probabilistic graph model nodes, utilizing correlations between modalities for inference. This research represents a significant step forward in the field, addressing a critical gap in the design of probabilistic graphical models for multi-modal data inference. ---
Author: Zhang, Jincheng
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7208807801