Credibility-Aware Multimodal Fusion Using Probabilistic Circuits
Abstract
We consider the problem of late multimodal 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 multimodal 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 being competitive with the state-of-the-art.
Cite
Text
Sidheekh et al. "Credibility-Aware Multimodal Fusion Using Probabilistic Circuits." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.Markdown
[Sidheekh et al. "Credibility-Aware Multimodal Fusion Using Probabilistic Circuits." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.](https://mlanthology.org/aistats/2025/sidheekh2025aistats-credibilityaware/)BibTeX
@inproceedings{sidheekh2025aistats-credibilityaware,
title = {{Credibility-Aware Multimodal Fusion Using Probabilistic Circuits}},
author = {Sidheekh, Sahil and Tenali, Pranuthi and Mathur, Saurabh and Blasch, Erik and Kersting, Kristian and Natarajan, Sriraam},
booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
year = {2025},
pages = {2305-2313},
volume = {258},
url = {https://mlanthology.org/aistats/2025/sidheekh2025aistats-credibilityaware/}
}