Adaptive Confidence Regularization for Multimodal Failure Detection

• URL: http://arxiv.org/abs/2603.02200v1
• Date: Mon, 02 Mar 2026 18:56:38 GMT
• Title: Adaptive Confidence Regularization for Multimodal Failure Detection
• Authors: Moru Liu, Hao Dong, Olga Fink, Mario Trapp,
• Abstract summary: We propose Adaptive Confidence Regularization (ACR), a framework specifically designed to detect multimodal failures.<n>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.<n>In addition, we propose Multimodal Feature Swapping, a novel outlier technique that generates challenging, failure-aware training examples.
• Score: 21.36399203050946
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Confidence Estimation for LLMs in Multi-turn Interactions [48.081802290688394]
  This work presents the first systematic study of confidence estimation in multi-turn interactions.<n>We establish a formal evaluation framework grounded in two key desideratas: per-turn calibration and monotonicity of confidence.<n>Our work provides a foundational methodology for developing more reliable and trustworthy conversational agents.
  arXiv  Detail & Related papers  (2026-01-05T14:58:04Z)
• Distributionally Robust Multimodal Machine Learning [1.8788768422083866]
  We propose a novel distributionally robust optimization (DRO) framework that aims to study both the theoretical and practical insights of multimodal machine learning.<n> Empirically, we demonstrate that our approach improves robustness in both simulation settings and real-world datasets.
  arXiv  Detail & Related papers  (2025-11-07T21:18:35Z)
• TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
  We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts.<n>Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
  arXiv  Detail & Related papers  (2025-04-20T09:20:55Z)
• Network Inversion for Generating Confidently Classified Counterfeits [11.599035626374409]
  In vision classification, generating inputs that elicit confident predictions is key to understanding model behavior and reliability.<n>We extend network inversion techniques to generate Confidently Classified Counterfeits (CCCs)<n>CCCs offer a model-centric perspective on confidence, revealing that models can assign high confidence to entirely synthetic, out-of-distribution inputs.
  arXiv  Detail & Related papers  (2025-03-26T03:26:49Z)
• Confidence-aware multi-modality learning for eye disease screening [58.861421804458395]
  We propose a novel multi-modality evidential fusion pipeline for eye disease screening. It provides a measure of confidence for each modality and elegantly integrates the multi-modality information. Experimental results on both public and internal datasets demonstrate that our model excels in robustness.
  arXiv  Detail & Related papers  (2024-05-28T13:27:30Z)
• Revisiting Confidence Estimation: Towards Reliable Failure Prediction [53.79160907725975]
  We find a general, widely existing but actually-neglected phenomenon that most confidence estimation methods are harmful for detecting misclassification errors. We propose to enlarge the confidence gap by finding flat minima, which yields state-of-the-art failure prediction performance.
  arXiv  Detail & Related papers  (2024-03-05T11:44:14Z)
• Multiclass Alignment of Confidence and Certainty for Network Calibration [10.15706847741555]
  Recent studies reveal that deep neural networks (DNNs) are prone to making overconfident predictions. We propose a new train-time calibration method, which features a simple, plug-and-play auxiliary loss known as multi-class alignment of predictive mean confidence and predictive certainty (MACC) Our method achieves state-of-the-art calibration performance for both in-domain and out-domain predictions.
  arXiv  Detail & Related papers  (2023-09-06T00:56:24Z)
• Calibrating Multimodal Learning [94.65232214643436]
  We propose a novel regularization technique, i.e., Calibrating Multimodal Learning (CML) regularization, to calibrate the predictive confidence of previous methods. This technique could be flexibly equipped by existing models and improve the performance in terms of confidence calibration, classification accuracy, and model robustness.
  arXiv  Detail & Related papers  (2023-06-02T04:29:57Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions [91.63716984911278]
  We introduce a novel Mixture of Normal-Inverse Gamma distributions (MoNIG) algorithm, which efficiently estimates uncertainty in principle for adaptive integration of different modalities and produces a trustworthy regression result. Experimental results on both synthetic and different real-world data demonstrate the effectiveness and trustworthiness of our method on various multimodal regression tasks.
  arXiv  Detail & Related papers  (2021-11-11T14:28:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.