Adaptive Modality Reliability Diagnosis and Restoration for Robust Multimodal Intent Recognition
Suraj Kumar, Mohnish Raj, Soumi Chattopadhayay, Chandranath Adak, Ayan Dutta
Why It Matters
What makes this one worth your time
This research is significant for improving the robustness of multimodal systems in real-world applications where data quality can vary greatly.
PRIME enhances multimodal intent recognition by diagnosing and restoring unreliable inputs.
Summary
The paper introduces PRIME, a framework for diagnosing, restoring, and reassessing the reliability of multimodal inputs in intent recognition, addressing issues of noisy or conflicting modalities.
Key contributions
- Development of a reliability-guided framework for multimodal intent recognition.
- Introduction of a prototype-conditioned variational restoration module.
- Implementation of a heteroscedastic uncertainty objective for training without modality-reliability annotations.
Notable insights
- The use of contextual log-variance to represent modality weakness is a novel approach to quantify reliability.
- The closed-loop mechanism of re-estimating reliability post-restoration allows for dynamic decision-making regarding modality trustworthiness.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2608.03475v1 Announce Type: cross Abstract: Multimodal intent recognition combines linguistic, acoustic, and visual evidence, but individual modalities may be noisy, missing, semantically conflicting, or disproportionately dominant. Existing methods typically infer modality importance implicitly and either reweight or suppress unreliable inputs, without determining whether a degraded modality can be repaired and subsequently trusted. We propose PRIME (Precision-weighted Reliability Inference and Modality rEstoration), a closed-loop reliability guided framework that jointly diagnoses, restores, and reassesses modality quality at the sample level. PRIME represents the weakness of each modality through a contextual log-variance estimated from complementary diagnostic evidence, including predictive confidence, epistemic disagreement, cross-modal consensus, and feature degeneracy. Because modality-reliability annotations are unavailable, the estimator is explicitly trained using controlled modality corruption with known degradation severity, together with a heteroscedastic uncertainty objective. Rather than directly discarding an unreliable modality, PRIME uses its estimated weakness to control a prototype-conditioned variational restoration module that reconstructs the degraded representation from complementary modalities. Crucially, reliability is re-estimated after restoration, allowing the model to determine whether the repaired representation has become sufficiently trustworthy to contribute to prediction. The resulting post-restoration precisions are used for inverse-variance multimodal fusion. Experiments on multimodal intent-recognition benchmarks show that PRIME maintains competitive clean-data performance while improving robustness under missing, noisy, conflicting, and modality-imbalanced conditions.