Multimodal Classification

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Overview

Multimodal classification studies how to combine different kinds of evidence—for example, medical images with clinical records, or meme images with text—to predict categories. Common approaches include feature or decision fusion, cross-attention and contrastive alignment, mixture-of-experts models, and large vision-language models; benchmarks range from medical diagnosis and sentiment analysis to document, time-series, and content moderation tasks. Current work also addresses missing or unreliable inputs through modality dropout, imputation, adaptive routing, and selective acquisition, while evaluating fairness, calibration, and interpretability. A central tension is that adding a modality can improve predictions when it contributes complementary information, but can also hurt when it is noisy, redundant, or unavailable at deployment.

Latest papers

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200 papers24 papers in the last four weeks, down 4% on the four weeks before. 0.1% of all new papers.

Apr 13, 2026

EmergentBridge: Improving Zero-Shot Cross-Modal Transfer in Unified Multimodal Embedding Models

Unified multimodal embedding spaces underpin practical applications such as cross-modal retrieval and zero-shot recognition. In many real deployments, however, supervision is available only for a small subset of modality pairs (e.g., image--text), leaving unpaired modality pairs (e.g., audio↔\leftrightarrowdepth, infrared↔\leftrightarrowaudio) weakly connected and thus performing poorly on zero-shot transfer. Addressing this sparse-pairing regime is therefore essential for scaling unified embedding systems to new tasks without curating exhaustive pairwise data. We propose EmergentBridge, an embedding-level bridging framework that improves performance on these unpaired pairs without requiring exhaustive pairwise supervision. Our key observation is that naively aligning a new modality to a synthesized proxy embedding can introduce gradient interference, degrading the anchor-alignment structure that existing retrieval/classification relies on. EmergentBridge addresses this by (i) learning a mapping that produces a noisy bridge anchor (a proxy embedding of an already-aligned modality) from an anchor embedding, and (ii) enforcing proxy alignment only in the subspace orthogonal to the anchor-alignment direction, preserving anchor alignment while strengthening non-anchor connectivity. Across nine datasets spanning multiple modalities, EmergentBridge consistently outperforms prior binding baselines on zero-shot classification and retrieval, demonstrating strong emergent alignment.

Jincheng Xie, Xingchen Xiao, Runheng Liu +3

School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China · JD iCity, JD Technology, JD Intelligent Cities Research, Beijing, China

Cross-Modal LearningMultimodal EmbeddingCross-Modal Representation TransferCross-Modal AlignmentCross-Modal RetrievalMultimodal Classification
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Apr 9, 2026

MONETA: Multimodal Industry Classification through Geographic Information with Multi Agent Systems

Industry classification schemes are integral parts of public and corporate databases as they classify businesses based on economic activity. Due to the size of the company registers, manual annotation is costly, and fine-tuning models with every update in industry classification schemes requires significant data collection. We replicate the manual expert verification by using existing or easily retrievable multimodal resources for industry classification. We present MONETA, the first multimodal industry classification benchmark with text (Website, Wikipedia, Wikidata) and geospatial sources (OpenStreetMap and satellite imagery). Our dataset enlists 1,000 businesses in Europe with 20 economic activity labels according to EU guidelines (NACE). Our training-free baseline reaches 62.10% and 74.10% with open and closed-source Multimodal Large Language Models (MLLM). We observe an increase of up to 22.80% with the combination of multi-turn design, context enrichment, and classification explanations. We will release our dataset and the enhanced guidelines.

Arda Yüksel, Gabriel Thiem, Susanne Walter +3

Trustworthy Human Language Technologies · Technical University of Darmstadt, Germany · Deutsche Bundesbank +2

ClassificationMultimodal ClassificationMultimodal DatasetMultimodal Large Language Models
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Apr 8, 2026

Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach

Digital forensic investigations increasingly rely on heterogeneous evidence such as images, scanned documents, and contextual reports. These artifacts may contain explicit or implicit expressions of harm, hate, threat, violence, or intimidation, yet existing automated approaches often assume clean text input or apply vision models without forensic justification. This paper presents a case-driven multimodal approach for hate and threat detection in forensic analysis. The proposed framework explicitly determines the presence and source of textual evidence, distinguishing between embedded text, associated contextual text, and image-only evidence. Based on the identified evidence configuration, the framework selectively applies text analysis, multimodal fusion, or image-only semantic reasoning using vision language models with vision transformer backbones (ViT). By conditioning inference on evidence availability, the approach mirrors forensic decision-making, improves evidentiary traceability, and avoids unjustified modality assumptions. Experimental evaluation on forensic-style image evidence demonstrates consistent and interpretable behavior across heterogeneous evidence scenarios.

Ponkoj Chandra Shill

Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA

Hate Speech DetectionDigital ForensicsMultimodal Classification
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Apr 8, 2026

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification

Kidney stone disease ranks among the most prevalent conditions in urology, and understanding the composition of these stones is essential for creating personalized treatment plans and preventing recurrence. Current methods for analyzing kidney stones depend on postoperative specimens, which prevents rapid classification before surgery. To overcome this limitation, we introduce a new approach called the Urinary Stone Segmentation and Classification Network (USCNet). This innovative method allows for precise preoperative classification of kidney stones by integrating Computed Tomography (CT) images with clinical data from Electronic Health Records (EHR). USCNet employs a Transformer-based multimodal fusion framework with CT-EHR attention and segmentation-guided attention modules for accurate classification. Moreover, a dynamic loss function is introduced to effectively balance the dual objectives of segmentation and classification. Experiments on an in-house kidney stone dataset show that USCNet demonstrates outstanding performance across all evaluation metrics, with its classification efficacy significantly surpassing existing mainstream methods. This study presents a promising solution for the precise preoperative classification of kidney stones, offering substantial clinical benefits. The source code has been made publicly available: https://github.com/ZhangSongqi0506/KidneyStone.

Changmiao Wang, Songqi Zhang, Yongquan Zhang +9

C. Wang and X. Wan are with Shenzhen Research Institute of Big Data, Shenzhen 518172, China · Y. Zhang, S. Zhang, and Y. Wang are with Zhejiang University of Finance and Economics, Hangzhou 310018, China · L. Liu is with Anhui University of Finance and Economics, Anhui 233000, China +4

Medical DiagnosisMedical Image ClassificationMultimodal ClassificationMultimodal FusionMedical Image Segmentation
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Apr 4, 2026

MultiPress: A Multi-Agent Framework for Interpretable Multimodal News Classification

With the growing prevalence of multimodal news content, effective news topic classification demands models capable of jointly understanding and reasoning over heterogeneous data such as text and images. Existing methods often process modalities independently or employ simplistic fusion strategies, limiting their ability to capture complex cross-modal interactions and leverage external knowledge. To overcome these limitations, we propose MultiPress, a novel three-stage multi-agent framework for multimodal news classification. MultiPress integrates specialized agents for multimodal perception, retrieval-augmented reasoning, and gated fusion scoring, followed by a reward-driven iterative optimization mechanism. We validate MultiPress on a newly constructed large-scale multimodal news dataset, demonstrating significant improvements over strong baselines and highlighting the effectiveness of modular multi-agent collaboration and retrieval-augmented reasoning in enhancing classification accuracy and interpretability.

Tailong Luo, Hao Li, Rong Fu +7

New York Institute of Technology · University of Arizona · University of Macau +3

Multimodal ClassificationMultimodal AgentsMultimodal FusionMultimodal Model Interpretability
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Mar 31, 2026

Multimodal Machine Learning for Early Prediction of Metastasis in a Swedish Multi-Cancer Cohort

Multimodal Machine Learning offers a holistic view of a patient's status, integrating structured and unstructured data from electronic health records (EHR). We propose a framework to predict metastasis risk one month prior to diagnosis, using six months of clinical history from EHR data. Data from four cancer cohorts collected at Karolinska University Hospital (Stockholm, Sweden) were analyzed: breast (n = 743), colon (n = 387), lung (n = 870), and prostate (n = 1890). The dataset included demographics, comorbidities, laboratory results, medications, and clinical text. We compared traditional and deep learning classifiers across single modalities and multimodal combinations, using various fusion strategies and a Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) 2a design, with an 80-20 development-validation split to ensure a rigorous, repeatable evaluation. Performance was evaluated using AUROC, AUPRC, F1 score, sensitivity, and specificity. We then employed a multimodal adaptation of SHAP to analyze the classifiers' reasoning. Intermediate fusion achieved the highest F1 scores on breast (0.845), colon (0.786), and prostate cancer (0.845), demonstrating strong predictive performance. For lung cancer, the intermediate fusion achieved an F1 score of 0.819, while the text-only model achieved the highest, with an F1 score of 0.829. Deep learning classifiers consistently outperformed traditional models. Colon cancer, the smallest cohort, had the lowest performance, highlighting the importance of sufficient training data. SHAP analysis showed that the relative importance of modalities varied across cancer types. Fusion strategies offer distinct strengths and weaknesses. Intermediate fusion consistently delivered the best results, but strategy choices should align with data characteristics and organizational needs.

Franco Rugolon, Korbinian Randl, Braslav Jovanovic +2

Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden

Clinical Outcome PredictionClinical Risk PredictionMultimodal ClassificationMultimodal Fusion
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Mar 28, 2026

SACRED: A Faithful Annotated Multimedia Multimodal Multilingual Dataset for Classifying Connectedness Types in Online Spirituality

In religion and theology studies, spirituality has garnered significant research attention for the reason that it not only transcends culture but offers unique experience to each individual. However, social scientists often rely on limited datasets, which are basically unavailable online. In this study, we collaborated with social scientists to develop a high-quality multimedia multi-modal datasets, SACRED, in which the faithfulness of classification is guaranteed. Using SACRED, we evaluated the performance of 13 popular LLMs as well as traditional rule-based and fine-tuned approaches. The result suggests DeepSeek-V3 model performs well in classifying such abstract concepts (i.e., 79.19% accuracy in the Quora test set), and the GPT-4o-mini model surpassed the other models in the vision tasks (63.99% F1 score). Purportedly, this is the first annotated multi-modal dataset from online spirituality communication. Our study also found a new type of connectedness which is valuable for communication science studies.

Qinghao Guan, Yuchen Pan, Donghao Li +6

Department of Communication and Media Research, University of Zurich, Zurich, Switzerland · Department of Computational Linguistics, University of Zurich, Zurich, Switzerland · Department of Informatics, University of Zurich, Zurich, Switzerland +1

Multimodal DatasetMultimodal ClassificationMultimodal Model Evaluation
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Mar 27, 2026

Material Identification using Multi-Modal Intrinsic Radiation and Radiography

We investigate multi-modal material identification for special nuclear material (SNM) configurations using a combination of X-ray radiography, high-resolution γ-ray spectroscopy, and neutron multiplicity measurements. We consider a Beryllium Reflected Plutonium sphere (BeRP) ball surrounded by one or two concentric shielding shells of unknown composition whose radii are assumed known from radiography. High-purity germanium (HPGe) spectra are reduced to net counts in selected Pu-239 photo-peaks, while neutron multiplicity information is summarized by Feynman variances Y2 and Y3 computed from factorial moments of the neutron counting statistics. Using synthetic data generated with the Gamma Detector Response and Analysis Software (GADRAS) for a range of shielding materials and thicknesses, we cast the material identification problem as a supervised multi-class classification task over all admissible shell-material combinations. We demonstrate that a random forest classifier trained on combined gamma and neutron features achieves almost perfect identification accuracy for single-shell cases, and substantial performance gains for more challenging double-shell configurations relative to gamma-only classification. Alternative statistical and machine-learning formulations for this multi-class problem are examined along with examination of the impact of model-mismatch between the forward model and the test cases as given by variations in the statistical noise. Opportunities for extending the approach to more complex geometries and experimental data are also discussed.

Khoa Nguyen, Brendt Wohlberg, Oleg Korobkin +1

Los Alamos National Laboratory, Los Alamos, NM 87545, USA

Multimodal ClassificationMultimodal Sensor Fusion
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Mar 27, 2026

Multimodal Deep Learning for Diabetic Foot Ulcer Staging Using Integrated RGB and Thermal Imaging

Diabetic foot ulcers (DFU) are one of the serious complications of diabetes that can lead to amputations and high healthcare costs. Regular monitoring and early diagnosis are critical for reducing the clinical burden and the risk of amputation. The aim of this study is to investigate the impact of using multimodal images on deep learning models for the classification of DFU stages. To this end, we developed a Raspberry Pi-based portable imaging system capable of simultaneously capturing RGB and thermal images. Using this prototype, a dataset consisting of 1,205 samples was collected in a hospital setting. The dataset was labeled by experts into six distinct stages. To evaluate the models performance, we prepared three different training sets: RGB-only, thermal-only, and RGB+Thermal (with the thermal image added as a fourth channel). We trained these training sets on the DenseNet121, EfficientNetV2, InceptionV3, ResNet50, and VGG16 models. The results show that the multimodal training dataset, in which RGB and thermal data are combined across four channels, outperforms single-modal approaches. The highest performance was observed in the VGG16 model trained on the RGB+Thermal dataset. The model achieved an accuracy of 93.25%, an F1-score of 92.53%, and an MCC of 91.03%. Grad-CAM heatmap visualizations demonstrated that the thermal channel helped the model focus on the correct location by highlighting temperature anomalies in the ulcer region, while the RGB channel supported the decision-making process with complementary structural and textural information.

Gulengul Mermer, Mustafa Furkan Aksu, Gozde Ozsezer +3

Multimodal ClassificationThermal Imaging
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Mar 26, 2026

Label What Matters: Modality-Balanced and Difficulty-Aware Multimodal Active Learning

Multimodal learning integrates complementary information from different modalities such as image, text, and audio to improve model performance, but its success relies on large-scale labeled data, which is costly to obtain. Active learning (AL) mitigates this challenge by selectively annotating informative samples. In multimodal settings, many approaches implicitly assume that modality importance is stable across rounds and keep selection rules fixed at the fusion stage, which leaves them insensitive to the dynamic nature of multimodal learning, where the relative value of modalities and the difficulty of instances shift as training proceeds. To address this issue, we propose RL-MBA, a reinforcement-learning framework for modality-balanced, difficulty-aware multimodal active learning. RL-MBA models sample selection as a Markov Decision Process, where the policy adapts to modality contributions, uncertainty, and diversity, and the reward encourages accuracy gains and balance. Two key components drive this adaptability: (1) Adaptive Modality Contribution Balancing (AMCB), which dynamically adjusts modality weights via reinforcement feedback, and (2) Evidential Fusion for DifficultyAware Policy Adjustment (EFDA), which estimates sample difficulty via uncertainty-based evidential fusion to prioritize informative samples. Experiments on Food101, KineticsSound, and VGGSound demonstrate that RL-MBA consistently outperforms strong baselines, improving both classification accuracy and modality fairness under limited labeling budgets.

Yuqiao Zeng, Xu Wang, Tengfei Liang +3

Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education; State Key Laboratory of Advanced Rail Autonomous Operation; School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China · School of Psychology & Neuroscience, University of Glasgow, Glasgow, UK

Active LearningMultimodal ClassificationUncertainty-Aware Multimodal FusionReinforcement Learning
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Mar 25, 2026

A Neuro-Symbolic System for Interpretable Multimodal Physiological Signals Integration in Human Fatigue Detection

We propose a neuro-symbolic architecture that learns four interpretable physiological concepts, oculomotor dynamics, gaze stability, prefrontal hemodynamics, and multimodal, from eye-tracking and neural hemodynamics, functional near-infrared spectroscopy, (fNIRS) windows using attention-based encoders, and combines them with differentiable approximate reasoning rules using learned weights and soft thresholds, to address both rigid hand-crafted rules and the lack of subject-level alignment diagnostics. We apply this system to fatigue classification from multimodal physiological signals, a domain that requires models that are accurate and interpretable, with internal reasoning that can be inspected for safety-critical use. In leave-one-subject-out evaluation on 18 participants (560 samples), the method achieves 72.1% +/- 12.3% accuracy, comparable to tuned baselines while exposing concept activations and rule firing strengths. Ablations indicate gains from participant-specific calibration (+5.2 pp), a modest drop without the fNIRS concept (-1.2 pp), and slightly better performance with Lukasiewicz operators than product (+0.9 pp). We also introduce concept fidelity, an offline per-subject audit metric from held-out labels, which correlates strongly with per-subject accuracy (r=0.843, p < 0.0001).

Mohammadreza Jamalifard, Yaxiong Lei, Parasto Azizinezhad +2

School of Computer Science and Electronic Engineering, University of Essex, United Kingdom

Multimodal Model InterpretabilityNeuro-Symbolic ReasoningMultimodal ClassificationMultimodal Sensor Fusion
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Mar 24, 2026

From Content to Audience: A Multimodal Annotation Framework for Broadcast Television Analytics

Automated semantic annotation of broadcast television content presents distinctive challenges, combining structured audiovisual composition, domain-specific editorial patterns, and strict operational constraints. While multimodal large language models (MLLMs) have demonstrated strong general-purpose video understanding capabilities, their comparative effectiveness across pipeline architectures and input configurations in broadcast-specific settings remains empirically undercharacterized. This paper presents a systematic evaluation of multimodal annotation pipelines applied to broadcast television news in the Italian setting. We construct a domain-specific benchmark of clips labeled across four semantic dimensions: visual environment classification, topic classification, sensitive content detection, and named entity recognition. Two different pipeline architectures are evaluated across nine frontier models, including Gemini 3.0 Pro, LLaMA 4 Maverick, Qwen-VL variants, and Gemma 3, under progressively enriched input strategies combining visual signals, automatic speech recognition, speaker diarization, and metadata. Experimental results demonstrate that gains from video input are strongly model-dependent: larger models effectively leverage temporal continuity, while smaller models show performance degradation under extended multimodal context, likely due to token overload. Beyond benchmarking, the selected pipeline is deployed on 14 full broadcast episodes, with minute-level annotations integrated with normalized audience measurement data provided by an Italian media company. This integration enables correlational analysis of topic-level audience sensitivity and generational engagement divergence, demonstrating the operational viability of the proposed framework for content-based audience analytics.

Paolo Cupini, Francesco Pierri

Politecnico di Milano

LLM-Assisted AnnotationMultimodal Large Language ModelsMultimodal ClassificationMultimodal Model Evaluation
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Mar 23, 2026

Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment

Unstructured Electronic Health Record (EHR) data, such as clinical notes, contain clinical contextual observations that are not directly reflected in structured data fields. This additional information can substantially improve model learning. However, due to their unstructured nature, these data are often unavailable or impractical to use when deploying a model. We introduce a multimodal learning framework that leverages unstructured EHR data during training while producing a model that can be deployed using only structured EHR data. Using a cohort of 3,466 children evaluated for late talking, we generated note embeddings with BioClinicalBERT and encoded structured embeddings from demographics and medical codes. A note-based teacher model and a structured-only student model were jointly trained using contrastive learning and contrastive knowledge distillation loss, producing a strong classifier (AUROC = 0.985). Our proposed model reached an AUROC of 0.705, outperforming the structured-only baseline of 0.656. These results demonstrate that incorporating unstructured data during training enhances the model's capacity to identify task-relevant information within structured EHR data, enabling a deployable structured-only phenotype model.

Zigui Wang, Minghui Sun, Jiang Shu +3

Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA · Duke Center for Autism and Brain Development, Duke University School of Medicine, Durham, NC, USA · Department of Psychiatry and Behavioral Sciences, Durham, NC, USA +1

Multimodal LearningMultimodal ClassificationElectronic Health RecordsMissing-Modality LearningCross-Modal Knowledge Distillation
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Mar 20, 2026

Improving Generalization on Cybersecurity Tasks with Multi-Modal Contrastive Learning

The use of ML in cybersecurity has long been impaired by generalization issues: Models that work well in controlled scenarios fail to maintain performance in production. The root cause often lies in ML algorithms learning superficial patterns (shortcuts) rather than underlying cybersecurity concepts. We investigate contrastive multi-modal learning as a first step towards improving ML performance in cybersecurity tasks. We aim at transferring knowledge from data-rich modalities, such as text, to data-scarce modalities, such as payloads. We set up a case study on threat classification and propose a two-stage multi-modal contrastive learning framework that uses textual vulnerability descriptions to guide payload classification. First, we construct a semantically meaningful embedding space using contrastive learning on descriptions. Then, we align payloads to this space, transferring knowledge from text to payloads. We evaluate the approach on a large-scale private dataset and a synthetic benchmark built from public CVE descriptions and LLM-generated payloads. The methodology appears to reduce shortcut learning over baselines on both benchmarks. We release our synthetic benchmark and source code as open source.

Jianan Huang, Rodolfo V. Valentim, Luca Vassio +4

Politecnico di Torino, Italy · Universit`a di Torino, Italy · Huawei Paris Research Center, France

Cross-Modal Representation TransferCybersecurityMultimodal Contrastive LearningMultimodal ClassificationShortcut LearningTransfer Learning
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Mar 20, 2026

CFCML: A Coarse-to-Fine Crossmodal Learning Framework For Disease Diagnosis Using Multimodal Images and Tabular Data

In clinical practice, crossmodal information including medical images and tabular data is essential for disease diagnosis. There exists a significant modality gap between these data types, which obstructs advancements in crossmodal diagnostic accuracy. Most existing crossmodal learning (CML) methods primarily focus on exploring relationships among high-level encoder outputs, leading to the neglect of local information in images. Additionally, these methods often overlook the extraction of task-relevant information. In this paper, we propose a novel coarse-to-fine crossmodal learning (CFCML) framework to progressively reduce the modality gap between multimodal images and tabular data, by thoroughly exploring inter-modal relationships. At the coarse stage, we explore the relationships between multi-granularity features from various image encoder stages and tabular information, facilitating a preliminary reduction of the modality gap. At the fine stage, we generate unimodal and crossmodal prototypes that incorporate class-aware information, and establish hierarchical anchor-based relationship mining (HRM) strategy to further diminish the modality gap and extract discriminative crossmodal information. This strategy utilize modality samples, unimodal prototypes, and crossmodal prototypes as anchors to develop contrastive learning approaches, effectively enhancing inter-class disparity while reducing intra-class disparity from multiple perspectives. Experimental results indicate that our method outperforms the state-of-the-art (SOTA) methods, achieving improvements of 1.53% and 0.91% in AUC metrics on the MEN and Derm7pt datasets, respectively. The code is available at https://github.com/IsDling/CFCML.

Tianling Liu, Hongying Liu, Fanhua Shang +3

College of Intelligence and Computing, Tianjin University, Tianjin 300350, China. · Medical School of Tianjin University, Tianjin 300072, China. · Peng Cheng Lab, Shenzhen 518055, China. +3

Cross-Modal LearningMultimodal ClassificationMedical DiagnosisMultimodal Contrastive Learning
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Mar 20, 2026

A Multimodal Deep Learning Framework for Edema Classification Using HCT and Clinical Data

We propose AttentionMixer, a unified deep learning framework for multimodal detection of brain edema that combines structural head CT (HCT) with routine clinical metadata. While HCT provides rich spatial information, clinical variables such as age, laboratory values, and scan timing capture complementary context that might be ignored or naively concatenated. AttentionMixer is designed to fuse these heterogeneous sources in a principled and efficient manner. HCT volumes are first encoded using a self-supervised Vision Transformer Autoencoder (ViT-AE++), without requiring large labeled datasets. Clinical metadata are mapped into the same feature space and used as keys and values in a cross-attention module, where HCT-derived feature vector serves as queries. This cross-attention fusion allows the network to dynamically modulate imaging features based on patient-specific context and provides an interpretable mechanism for multimodal integration. A lightweight MLP-Mixer then refines the fused representation before final classification, enabling global dependency modeling with substantially reduced parameter overhead. Missing or incomplete metadata are handled via a learnable embedding, promoting robustness to real-world clinical data quality. We evaluate AttentionMixer on a curated brain HCT cohort with expert edema annotations using five-fold cross-validation. Compared with strong HCT-only, metadata-only, and prior multimodal baselines, AttentionMixer achieves superior performance (accuracy 87.32%, precision 92.10%, F1-score 85.37%, AUC 94.14%). Ablation studies confirm the benefit of both cross-attention and MLP-Mixer refinement, and permutation-based metadata importance analysis highlights clinically meaningful variables driving predictions. These results demonstrate that structured, interpretable multimodal fusion can substantially improve edema detection in clinical practice.

Aram Ansary Ogholbake, Hannah Choi, Spencer Brandenburg +10

Medical Image ClassificationMultimodal ClassificationMultimodal FusionCross-Modal Attention
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Mar 19, 2026

Cross-Modal Rationale Transfer for Explainable Humanitarian Classification on Social Media

Advances in social media data dissemination enable the provision of real-time information during a crisis. The information comes from different classes, such as infrastructure damages, persons missing or stranded in the affected zone, etc. Existing methods attempted to classify text and images into various humanitarian categories, but their decision-making process remains largely opaque, which affects their deployment in real-life applications. Recent work has sought to improve transparency by extracting textual rationales from tweets to explain predicted classes. However, such explainable classification methods have mostly focused on text, rather than crisis-related images. In this paper, we propose an interpretable-by-design multimodal classification framework. Our method first learns the joint representation of text and image using a visual language transformer model and extracts text rationales. Next, it extracts the image rationales via the mapping with text rationales. Our approach demonstrates how to learn rationales in one modality from another through cross-modal rationale transfer, which saves annotation effort. Finally, tweets are classified based on extracted rationales. Experiments are conducted over CrisisMMD benchmark dataset, and results show that our proposed method boosts the classification Macro-F1 by 2-35% while extracting accurate text tokens and image patches as rationales. Human evaluation also supports the claim that our proposed method is able to retrieve better image rationale patches (12%) that help to identify humanitarian classes. Our method adapts well to new, unseen datasets in zero-shot mode, achieving an accuracy of 80%.

Thi Huyen Nguyen, Koustav Rudra, Wolfgang Nejdl

L3S Research Center, Leibniz University Hannover, Hannover, Germany · Indian Institute of Technology Kharagpur, Kharagpur, India

ClassificationMultimodal ClassificationMultimodal Model InterpretabilityExplainable Image Classification
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Mar 18, 2026

Harm or Humor: A Multimodal, Multilingual Benchmark for Overt and Covert Harmful Humor

Dark humor often relies on subtle cultural nuances and implicit cues that require contextual reasoning to interpret, posing safety challenges that current static benchmarks fail to capture. To address this, we introduce a novel multimodal, multilingual benchmark for detecting and understanding harmful and offensive humor. Our manually curated dataset comprises 3,000 texts and 6,000 images in English and Arabic, alongside 1,200 videos that span English, Arabic, and language-independent (universal) contexts. Unlike standard toxicity datasets, we enforce a strict annotation guideline: distinguishing Safe jokes from Harmful ones, with the latter further classified into Explicit (overt) and Implicit (Covert) categories to probe deep reasoning. We systematically evaluate state-of-the-art (SOTA) open and closed-source models across all modalities. Our findings reveal that closed-source models significantly outperform open-source ones, with a notable difference in performance between the English and Arabic languages in both, underscoring the critical need for culturally grounded, reasoning-aware safety alignment. Warning: this paper contains example data that may be offensive, harmful, or biased.

Ahmed Sharshar, Hosam Elgendy, Saad El Dine Ahmed +2

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE · INSAIT, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria

LLM Safety BenchmarksLanguage Model Safety EvaluationMultilingual Language Model EvaluationMultimodal ClassificationCross-Cultural Language Model Evaluation
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Mar 18, 2026

Early Intervention for VFM-based Multimodal Medical Image Classification

Current methods for multimodal medical image classification (M3IC) face two major challenges. First, the prevailing "fusion after unimodal image embedding" paradigm cannot fully exploit the complementary and correlated information in the multimodal data. Second, the scarcity of labeled multimodal medical images, coupled with their substantial domain shift from natural images, impedes the use of cutting-edge Vision Foundation Models (VFMs) for medical image embedding. To jointly address the challenges, we propose a novel Early Intervention (EI) framework. Treating one modality as target and the rest as reference, EI harnesses high-level semantic tokens from the reference as intervention tokens to steer the target modality's embedding process at an early stage. For parameter-efficient VFM adaptation, we introduce Mixture of varied-rank LoRAs (MoR), which employs a small set of LoRA experts with distinct ranks and a bypass-allowed router. Extensive experiments on three public datasets, covering retinal disease recognition, skin lesion recognition, and knee anomaly classification, verify the effectiveness of the proposed method against a number of competitive baselines.

Qijie Wei, Hailan Lin, Xirong Li

Renmin University of China, Beijing 100872, China

Cross-Modal Representation LearningVision Foundation Model AdaptationLow-Rank AdaptationMultimodal ClassificationMedical Image ClassificationMultimodal Fusion
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Mar 18, 2026

3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation

Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual 2D slices extracted from MRI volumes, while clinical neuroimaging practice typically relies on the full three dimensional structure of the brain. From this perspective, volumetric analysis may better capture spatial relationships among brain regions that are relevant to disease progression. Motivated by this idea, this work proposes a multimodal 3D convolutional neural network for AD classification using raw OASIS 1 MRI volumes. The model combines structural T1 information with gray matter, white matter, and cerebrospinal fluid probability maps obtained through FSL FAST segmentation in order to capture complementary neuroanatomical information. The proposed approach is evaluated on the clinically labelled OASIS 1 cohort using 5 fold subject level cross validation, achieving a mean accuracy of 72.34% plus or minus 4.66% and a ROC AUC of 0.7781 plus or minus 0.0365. GradCAM visualizations further indicate that the model focuses on anatomically meaningful regions, including the medial temporal lobe and ventricular areas that are known to be associated with Alzheimer's related structural changes. To better understand how data representation and evaluation strategies may influence reported performance, additional diagnostic experiments were conducted on a slice based version of the dataset under both slice level and subject level protocols. These observations help provide context for the volumetric results. Overall, the proposed multimodal 3D framework establishes a reproducible subject level benchmark and highlights the potential benefits of volumetric MRI analysis for Alzheimer's disease classification.

Md Sifat, Sania Akter, Akif Islam +5

Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh · School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Japan · Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh

Alzheimer's Disease ClassificationMultimodal Classification3D CNNsMedical Image Benchmarks
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Mar 17, 2026

Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data

Accurate diagnosis of Alzheimer's disease (AD) requires handling tabular biomarker data, yet such data are often small and incomplete, where deep learning models frequently fail to outperform classical methods. Pretrained large language models (LLMs) offer few-shot generalization, structured reasoning, and interpretable outputs, providing a powerful paradigm shift for clinical prediction. We propose TAP-GPT Tabular Alzheimer's Prediction GPT, a domain-adapted tabular LLM framework built on TableGPT2 and fine-tuned for few-shot AD classification using tabular prompts rather than plain texts. We evaluate TAP-GPT across four ADNI-derived datasets, including QT-PAD biomarkers and region-level structural MRI, amyloid PET, and tau PET for binary AD classification. Across multimodal and unimodal settings, TAP-GPT improves upon its backbone models and outperforms traditional machine learning baselines in the few-shot setting while remaining competitive with state-of-the-art general-purpose LLMs. We show that feature selection mitigates degradation in high-dimensional inputs and that TAP-GPT maintains stable performance under simulated and real-world missingness without imputation. Additionally, TAP-GPT produces structured, modality-aware reasoning aligned with established AD biology and shows greater stability under self-reflection, supporting its use in iterative multi-agent systems. To our knowledge, this is the first systematic application of a tabular-specialized LLM to multimodal biomarker-based AD prediction, demonstrating that such pretrained models can effectively address structured clinical prediction tasks and laying the foundation for tabular LLM-driven multi-agent clinical decision-support systems. The source code is publicly available on GitHub: https://github.com/sophie-kearney/TAP-GPT.

Sophie Kearney, Shu Yang, Zixuan Wen +8

University of Pennsylvania, Philadelphia, PA, USA · Stony Brook University, Stony Brook, NY, USA · University of North Carolina at Chapel Hill, Chapel Hill, NC, USA +2

Alzheimer's Disease ClassificationTabular Foundation ModelsMultimodal ClassificationMultimodal Model Interpretability
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Mar 16, 2026

Data-Local Autonomous LLM-Guided Neural Architecture Search for Multiclass Multimodal Time-Series Classification

Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop: Performance depends strongly on preprocessing and architecture, yet training often has to run on-premise under strict data-local constraints. This is a common problem in healthcare and other privacy-constrained domains (e.g., a hospital developing deep learning models on patient EEG). This bottleneck is particularly challenging in multimodal fusion, where sensor modalities must be individually preprocessed and then combined. LLM-guided neural architecture search (NAS) can automate this exploration, but most existing workflows assume cloud execution or access to data-derived artifacts that cannot be exposed. We present a novel data-local, LLM-guided search framework that handles candidate pipelines remotely while executing all training and evaluation locally under a fixed protocol. The controller observes only trial-level summaries, such as pipeline descriptors, metrics, learning-curve statistics, and failure logs, without ever accessing raw samples or intermediate feature representations. Our framework targets multiclass, multimodal learning via one-vs-rest binary experts per class and modality, a lightweight fusion MLP, and joint search over expert architectures and modality-specific preprocessing. We evaluate our method on two regimes: UEA30 (public multivariate time-series classification dataset) and SleepEDFx sleep staging (heterogeneous clinical modalities such as EEG, EOG, and EMG). The results show that the modular baseline model is strong, and the LLM-guided NAS further improves it. Notably, our method finds models that perform within published ranges across most benchmark datasets. Across both settings, our method reduces manual intervention by enabling unattended architecture search while keeping sensitive data on-premise.

Emil Hardarson, Luka Biedebach, Ómar Bessi Ómarsson +3

Reykjavik University, Reykjavik, Iceland · University of Southampton, Southampton, United Kingdom

Large Language Model-Guided NASMultivariate Time Series ClassificationMultimodal ClassificationPrivacy-Preserving ML
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Mar 16, 2026

DamageArbiter: A Multimodal Arbitration Framework for Disaster Damage Assessment from Street-View Imagery

Analyzing street-view imagery with computer vision models offers a promising approach for rapid, hyperlocal disaster damage assessment, but existing approaches typically rely on black-box pre-trained vision models, which lack interpretability and reliability. This study proposes DamageArbiter, a multimodal disagreement-driven arbitration framework designed to improve the accuracy and reliability of street-view-based damage assessment. DamageArbiter leverages the complementary strengths of unimodal and multimodal models and employs a lightweight logistic regression meta-classifier to arbitrate cases in which model predictions disagree. Using 2,556 post-disaster street-view images, paired with manually generated or large language model (LLM)-generated text descriptions, we systematically compared DamageArbiter with fine-tuned unimodal (image-only and text-only) models and CLIP-based multimodal models in terms of classification performance and overconfidence errors. Results show that DamageArbiter improved accuracy to 75.85% and the Matthews correlation coefficient (MCC) to 0.6188, compared with the best-performing text-only baseline (63.07% accuracy, 0.4126 MCC), image-only baseline (74.33% accuracy, 0.5947 MCC), and CLIP baseline (74.22% accuracy, 0.5915 MCC). The overconfidence analysis further reveals that DamageArbiter substantially reduced the overconfidence error from 70.58% for the best-performing baseline, the image-only ViT model, to 16.45%. Overall, this study demonstrates that accuracy alone is insufficient for evaluating disaster damage classification models and highlights the importance of measuring overconfidence errors as part of model reliability assessment. DamageArbiter thus offers a more reliable framework for rapid, hyperlocal disaster damage assessment from street-view imagery.

Yifan Yang, Lei Zou, Wenjing Gong +6

Department of Geography, Texas A&M University, College Station, USA · Department of Landscape Architecture & Urban Planning, Texas A&M University, College Station, USA · Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA +2

Disaster Damage AssessmentMultimodal ClassificationMultimodal Model Evaluation
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Mar 13, 2026

Team LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach

Ambivalence/hesitancy recognition in unconstrained videos is a challenging problem due to the subtle, multimodal, and context-dependent nature of this behavioral state. In this paper, a multimodal approach for video-level ambivalence/hesitancy recognition is presented for the 10th ABAW Competition. The proposed approach integrates four complementary modalities: scene, face, audio, and text. Scene dynamics are captured with a VideoMAE-based model, facial information is encoded through emotional frame-level embeddings aggregated by statistical pooling, acoustic representations are extracted with EmotionWav2Vec2.0 and processed by a Mamba-based temporal encoder, and linguistic cues are modeled using fine-tuned transformer-based text models. The resulting unimodal embeddings are further combined using multimodal fusion models, including prototype-augmented variants. Experiments on the BAH corpus demonstrate clear gains of multimodal fusion over all unimodal baselines. The best unimodal configuration achieved an average MF1 of 70.02%, whereas the best multimodal fusion model reached 83.25%. The highest final test performance, 71.43%, was obtained by an ensemble of five prototype-augmented fusion models. The obtained results highlight the importance of complementary multimodal cues and robust fusion strategies for ambivalence/hesitancy recognition.

Elena Ryumina, Alexandr Axyonov, Dmitry Sysoev +4

St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia · HSE University, St. Petersburg, Russia

Ambivalence and Hesitancy RecognitionMultimodal ClassificationMultimodal Fusion
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Mar 13, 2026

GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification

We propose glaucoma lesion evaluation and analysis with multimodal imaging (GLEAM), the first publicly available tri-modal glaucoma dataset comprising scanning laser ophthalmoscopy fundus images, circumpapillary OCT images, and visual field pattern deviation maps, annotated with four disease stages, enabling effective exploitation of multimodal complementary information and facilitating accurate diagnosis and treatment across disease stages. To effectively integrate cross-modal information, we propose hierarchical attentive masked modeling (HAMM) for multimodal glaucoma classification. Our framework employs hierarchical attentive encoders and light decoders to focus cross-modal representation learning on the encoder.

Jiao Wang, Chi Liu, Yiying Zhang +8

College of the Information Science and Engineering, Northeastern University, Shenyang 110819, China · Department of Ophthalmology, Shenyang Fourth People’s Hospital, Shenyang 110000, China

Glaucoma DetectionOphthalmic Image AnalysisOphthalmologyMultimodal ClassificationMultimodal LearningMultimodal Fusion+1
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Mar 12, 2026

Multimodal classification of Radiation-Induced Contrast Enhancements and tumor recurrence using deep learning

The differentiation between tumor recurrence and radiation-induced contrast enhancements in post-treatment glioblastoma patients remains a major clinical challenge. Existing approaches rely on clinically sparsely available diffusion MRI or do not consider radiation maps, which are gaining increasing interest in the tumor board for this differentiation. We introduce RICE-NET, a multimodal 3D deep learning model that integrates longitudinal MRI data with radiotherapy dose distributions for automated lesion classification using conventional T1-weighted MRI data. Using a cohort of 92 patients, the model achieved an F1 score of 0.92 on an independent test set. During extensive ablation experiments, we quantified the contribution of each timepoint and modality and showed that reliable classification largely depends on the radiation map. Occlusion-based interpretability analyses further confirmed the model's focus on clinically relevant regions. These findings highlight the potential of multimodal deep learning to enhance diagnostic accuracy and support clinical decision-making in neuro-oncology.

Robin Peretzke, Marlin Hanstein, Maximilian Fischer +15

Deutsches Krebsforschungszentrum (DKFZ), Division of Medical Image Computing, Heidelberg, Germany · Medical Faculty, Heidelberg University, Heidelberg, Germany · Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany +8

Multimodal ClassificationClassificationBrain Tumor Classification
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Mar 9, 2026

Multimodal Deep Learning for Dynamic and Static Neuroimaging: Integrating MRI and fMRI for Alzheimer Disease Analysis

Magnetic Resonance Imaging (MRI) provides detailed structural information, while functional MRI (fMRI) captures temporal brain activity. In this work, we present a multimodal deep learning framework that integrates MRI and fMRI for multi-class classification of Alzheimer Disease (AD), Mild Cognitive Impairment, and Normal Cognitive State. Structural features are extracted from MRI using 3D convolutional neural networks, while temporal features are learned from fMRI sequences using recurrent architectures. These representations are fused to enable joint spatial-temporal learning. Experiments were conducted on a small paired MRI-fMRI dataset (29 subjects), both with and without data augmentation. Results show that data augmentation substantially improves classification stability and generalization, particularly for the multimodal 3DCNN-LSTM model. In contrast, augmentation was found to be ineffective for a large-scale single-modality MRI dataset. These findings highlight the importance of dataset size and modality when designing augmentation strategies for neuroimaging-based AD classification.

Anima Kujur, Zahra Monfared

Interdisciplinary Centre for Scientific Computing (IWR), Department of Mathematics and Computer Science, Heidelberg University, Germany

Alzheimer's Disease ClassificationNeuroimagingMultimodal ClassificationMultimodal FusionData Augmentation
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Mar 6, 2026

Multimodal Large Language Models as Image Classifiers

Multimodal Large Language Model (MLLM) classification performance depends critically on evaluation protocol and ground truth quality. Studies comparing MLLMs with supervised and Vision-Language Models (VLMs) report conflicting conclusions, and we show these conflicts stem from protocols that either inflate or underestimate performance. Across the most common evaluation protocols, we identify and fix key issues: model outputs that fall outside the provided class list and are discarded, inflated results from weak multiple-choice distractors, and open-world setting that underperforms only due to poor output mapping. We additionally quantify the impact of commonly overlooked design choices - batch size, image ordering, and text encoder selection - showing they substantially affect accuracy. Evaluating on ReGT, our multilabel reannotation of 625 ImageNet-1k classes, reveals that MLLMs benefit most from corrected labels (up to +10.8%), substantially narrowing the perceived gap with supervised models. Much of the reported MLLM underperformance on classification is thus an artifact of noisy ground truth and flawed evaluation protocol rather than genuine model deficiency. Models less reliant on supervised training signals prove most sensitive to annotation quality. Finally, we show that MLLMs can assist human annotators: in a controlled case study, annotators confirmed or integrated MLLM predictions in approximately 50% of difficult cases, demonstrating their potential for large-scale dataset curation. This work is part of the Aiming for Perfect ImageNet-1k project, see https://klarajanouskova.github.io/ImageNet/.

Nikita Kisel, Illia Volkov, Klara Janouskova +1

Visual Recognition Group, Czech Technical University in Prague

Multimodal Large Language ModelsMultimodal ClassificationMultimodal Model EvaluationImage Classification
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Mar 6, 2026

XMACNet: An Explainable Lightweight Attention based CNN with Multi Modal Fusion for Chili Disease Classification

Plant disease classification via imaging is a critical task in precision agriculture. We propose XMACNet, a novel light-weight Convolutional Neural Network (CNN) that integrates self-attention and multi-modal fusion of visible imagery and vegetation indices for chili disease detection. XMACNet uses an EfficientNetV2S backbone enhanced by a self-attention module and a fusion branch that processes both RGB images and computed vegetation index maps (NDVI, NPCI, MCARI). We curated a new dataset of 12,000 chili leaf images across six classes (five disease types plus healthy), augmented synthetically via StyleGAN to mitigate data scarcity. Trained on this dataset, XMACNet achieves high accuracy, F1-score, and AUC, outperforming baseline models such as ResNet-50, MobileNetV2, and a Swin Transformer variant. Crucially, XMACNet is explainable: we use Grad-CAM++ and SHAP to visualize and quantify the models focus on disease features. The models compact size and fast inference make it suitable for edge deployment in real-world farming scenarios.

Tapon Kumer Ray, Rajkumar Y, Shalini R +3

Student, Vellore Institute of Technology, Amaravati, Andhra Pradesh, India. · Assistant Professor, Vellore Institute of Technology, Amaravati, Andhra Pradesh, India. · Student, Angel College of Engineering and Technology, Tiruppur, Tamilnadu, India +2

Plant Disease ClassificationLightweight CNNsMultimodal ClassificationExplainable Image Classification
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Mar 3, 2026

An Empirical Analysis of Calibration and Selective Prediction in Multimodal Clinical Condition Classification

As artificial intelligence systems move toward clinical deployment, ensuring reliable prediction behavior is fundamental for safety-critical decision-making tasks. One proposed safeguard is selective prediction, where models can defer uncertain predictions to human experts for review. In this work, we empirically evaluate the reliability of uncertainty-based selective prediction in multilabel clinical condition classification using multimodal ICU data. Across a range of state-of-the-art unimodal and multimodal models, we find that selective prediction can substantially degrade performance despite strong standard evaluation metrics. This failure is driven by severe class-dependent miscalibration, whereby models assign high uncertainty to correct predictions and low uncertainty to incorrect ones, particularly for underrepresented clinical conditions. Our results show that commonly used aggregate metrics can obscure these effects, limiting their ability to assess selective prediction behavior in this setting. Taken together, our findings characterize a task-specific failure mode of selective prediction in multimodal clinical condition classification and highlight the need for calibration-aware evaluation to provide strong guarantees of safety and robustness in clinical AI.

L. Julián Lechuga López, Farah E. Shamout, Tim G. J. Rudner

New York University · New York University Abu Dhabi · University of Toronto

Selective PredictionUncertainty CalibrationMultimodal Model EvaluationMulti-Label ClassificationMultimodal Classification
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