Error Detection for Multimodal Classification (2025)

Abstract: Machine learning models have proven to be useful in various key applications such as autonomous driving or diagnosis prediction. When a model is implemented under real-world conditions, it is thus essential to detect potential errors with a trustworthy approach. This monitoring practice will render decision-making safer by avoiding catastrophic failures. In this paper, the focus is on multimodal classification. We introduce a method that addresses error detection based on unlabeled data. It leverages fused representations and computes the probability that a model will fail based on detected fault patterns in validation data. To improve transparency, we employ a sampling-based approximation of Shapley values in multimodal settings in order to explain why a prediction is assessed as erroneous in terms of feature values. Further, as explanation methods can sometimes disagree, we suggest evaluating the consistency of explanations produced by different value functions and algorithms. To show the relevance of our method, we measure it against a selection of 9 baselines from various domains on tabular-text and text-image datasets, and 2 multimodal fusion strategies for the classification models. Lastly, we show the usefulness of our explanation algorithm on misclassified samples.

## introduction even though pretrained language models such as bert can achieve state-of-the-art performance in various nlp tasks such as classification @xcite , they still have significant limitations @xcite : they do not always provide reliable uncertainty estimates, they are sensitive to distribution shifts and adversarial attacks, and their decisions are not fully transparent. in that context, it is key to employ monitoring tools that will render decision-making safer in applications where the costs of ai errors can be significant. error detection attempts to identify mispredicted test inputs @xcite . therefore, when a model is implemented for critical applications such as diagnosis prediction, autonomous driving or financial investment, it is essential to detect and avoid erroneous predictions that could have severe consequences. when potential failure is identified, the final decision could be, for instance, overridden by subject matter experts. in that case, it can be valuable to explain why a prediction is assessed as erroneous, in terms of feature values. here the focus is on multimodal classification tasks. even though our method could be applied to various types of modalities, we illustrate its relevance in a bimodal context. firstly, we concentrate on tabular datasets with text fields in english. these datasets consist of categorical and numerical features (i.e. the tabular modality) and fields with free-form text (i.e. the text modality) @xcite . categorical variables have discrete values (e.g. ordinal, binary or variable with finite number of categories) whereas numerical or quantitative variables have continuous scalar values. secondly, we consider applications with text and image modalities. various critical applications rely on such datasets. in the medical field, clinical notes and mri data could be employed for diagnosis prediction. in financial investment, models could make decisions based on time series (e.g. asset price) and text news for sentiment analysis. our objective is to evaluate what method may perform best with regard to error detection for multimodal classifiers. our approach is illustrated in figure 1 . we highlight the following contributions: ## prior work in this section, we summarize the prior work related to error detection. we also describe methods from closely connected domains: out-of-distribution (ood) detection and uncertainty estimation. ## method we have a c-class classification problem, where each input @xmath0. the true class is @xmath1, 1, . . . , c -1}. we consider a source dataset @xmath2, y i )} n @xmath3, which includes n points sampled i.i.d. from distribution p. further, d is randomly partitioned into a training dataset d train and a validation dataset d val . we consider a class of hypotheses h mapping x to ∆ c-1 , where ∆ c-1 is the probability simplex over c classes. given a classifier π ∈ h fitted on d train , the predicted label is ŷ = arg max @xmath4, ∀@xmath5. further, the 0-1 loss is defined as l(π(x), y) = 1 y̸ =ŷ , where 1 condition is 1 if the condition is true, 0 otherwise. we assume we can extract feature embeddings z from the model π: we have @xmath6, where the multimodal feature encoder ϕ : @xmath7.g. late fusion), and d is the embedding dimension. for example, z could be the concatenation of the [cls] tokens' final hidden states from the two modalities (see figure 1 ), where @xcite corresponds to the classification token defined by @xcite . given π and unlabeled test dataset t , our objective is to identify mispredicted inputs. ## experiments we empirically test the relevance of our method on various classification datasets. in the appendix, we provide further details on the experimental settings and results (e.g. datasets, data preprocessing, multimodal architectures, baselines, variability in results). ## conclusion we introduced a method to compute and explain the likelihood of failure in multimodal classification tasks. we compared our method to 9 baselines and evidenced that edip can be a useful approach to identify misclassified inputs. detecting errors and providing explanations to subject matter experts is a first step toward safer machine learning systems. with this type of human-ai interaction, experts can thus make more informed decisions, justify their choice, and override the classifier's output if necessary. future work could address the case of other modalities and multimodal ood settings.