Adaptive Confidence Regularization for Multimodal Failure Detection

2026-03-02

AI Summary

This work addresses the critical lack of reliable failure detection mechanisms in multimodal models deployed in high-stakes scenarios. It presents the first systematic study of multimodal failure detection, uncovering a phenomenon termed “confidence degradation”—where multimodal predictions exhibit lower confidence than their unimodal counterparts. To mitigate this issue, the authors propose an adaptive confidence regularization framework that introduces a novel adaptive confidence loss and leverages cross-modal feature swapping to synthesize failure samples during training. This approach enhances the model’s ability to recognize and reject uncertain predictions. Extensive experiments across four datasets, three modalities, and diverse evaluation settings demonstrate consistent and robust performance improvements, significantly boosting the reliability of multimodal systems.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

The deployment of multimodal models in high-stakes domains, such as self-driving vehicles and medical diagnostics, demands not only strong predictive performance but also reliable mechanisms for detecting failures. In this work, we address the largely unexplored problem of failure detection in multimodal contexts. We propose Adaptive Confidence Regularization (ACR), a novel framework specifically designed to detect multimodal failures. Our approach is driven by a key observation: in most failure cases, the confidence of the multimodal prediction is significantly lower than that of at least one unimodal branch, a phenomenon we term confidence degradation. To mitigate this, we introduce an Adaptive Confidence Loss that penalizes such degradations during training. In addition, we propose Multimodal Feature Swapping, a novel outlier synthesis technique that generates challenging, failure-aware training examples. By training with these synthetic failures, ACR learns to more effectively recognize and reject uncertain predictions, thereby improving overall reliability. Extensive experiments across four datasets, three modalities, and multiple evaluation settings demonstrate that ACR achieves consistent and robust gains. The source code will be available at https://github.com/mona4399/ACR.

Problem

• multimodal failure detection
• confidence degradation
• failure detection
• multimodal models
• reliable prediction

Innovation

• Adaptive Confidence Regularization
• Multimodal Failure Detection
• Confidence Degradation
• Multimodal Feature Swapping
• Outlier Synthesis