Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits
arXiv:2603.26629v2 Announce Type: replace Abstract: Multimodal fusion requires integrating information from multiple sources that may conflict depending on context. Existing fusion approaches typically rely on static assumptions about source reliability, limiting their ability to resolve conflicts when a modality becomes unreliable due to situational factors such as sensor degradation or class-specific corruption. We introduce C$^2$MF, a context-specfic credibility-aware multimodal fusion framework that models per-instance source reliability using a Conditional Probabilistic Circuit (CPC). We
The increasing complexity and deployment of AI systems in real-world, dynamic environments necessitates more robust and adaptable multimodal fusion techniques.
This research addresses a critical limitation in current AI systems, enabling them to make more reliable decisions by intelligently handling conflicting or unreliable sensor data, which is crucial for applications where trust is paramount.
AI models can now dynamically assess and adapt to the credibility of different data sources, leading to more resilient and context-aware decision-making instead of relying on static assumptions.
- · Autonomous systems developers
- · Robotics industry
- · Defense contractors utilizing AI
- · AI ethics and safety researchers
- · Developers relying solely on static multimodal fusion
- · Systems highly vulnerable to sensor degradation
More robust AI applications in critical domains like autonomous vehicles and surveillance.
Reduced incidence of AI failures due to compromised or misleading sensor inputs, increasing public trust.
Accelerated development of AI systems capable of operating in highly contested or unpredictable environments.
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Read at arXiv cs.LG