Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929423400304640 |
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| author | Sidheekh, Sahil Tenali, Pranuthi Mathur, Saurabh Blasch, Erik Kersting, Kristian Natarajan, Sriraam |
| author_facet | Sidheekh, Sahil Tenali, Pranuthi Mathur, Saurabh Blasch, Erik Kersting, Kristian Natarajan, Sriraam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03281 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits Sidheekh, Sahil Tenali, Pranuthi Mathur, Saurabh Blasch, Erik Kersting, Kristian Natarajan, Sriraam Machine Learning Artificial Intelligence 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. |
| title | Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.03281 |