Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits
Sahil Sidheekh Pranuthi Tenali Saurabh Mathur Erik Blasch Kristian Kersting Sriraam Natarajan
We consider the problem of late multi-modal fusion for discriminative
learning. Motivated by noisy, multi-source domains that require understanding
the reliability of each data source, we explore the notion of credibility in
the context of multi-modal fusion. We propose a combination function that uses
probabilistic circuits (PCs) to combine predictive distributions over
individual modalities. We also define a probabilistic measure to evaluate the
credibility of each modality via inference queries over the PC. Our
experimental evaluation demonstrates that our fusion method can reliably infer
credibility while maintaining competitive performance with the
state-of-the-art.