Multimodal Fusion

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Overview

Multimodal fusion studies how to combine inputs such as images, text, audio, medical scans, sensor streams, and structured records so models can use their complementary information. Common approaches include early or late fusion, cross-attention and contrastive alignment, and adaptive gates or mixtures of experts that weight modalities by task, sample, or estimated reliability. Methods are tested across settings such as BraTS medical segmentation, CMU-MOSI and CMU-MOSEI sentiment analysis, RGB-thermal detection, and clinical prediction, with growing attention to missing, noisy, or misaligned inputs. A central open tension is that additional modalities can help when they add useful evidence, but can also introduce noise or let a dominant modality suppress weaker ones; robust gains and generalization beyond benchmark conditions remain difficult to establish.

Latest papers

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771 papers73 papers in the last four weeks, down 12% on the four weeks before. 0.4% of all new papers.

Sep 11, 2026

Beyond Ambiguous Visual Cues: Studying Physiological Disruptions and Cross-Modal Inconsistencies in Deepfake Videos

Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks lack physiological ground truth, and current detectors underexplore the cross-level relationship between facial features and physiological dynamics, often relying on late fusion or rPPG features alone. In this paper, we construct high-fidelity deepfake manipulations on established real rPPG datasets (COHFACE and UBFC-rPPG) to investigate how forgeries disrupt natural physiological signals and facial behavior at the same time. Building on this analysis, we propose a bidirectional co-attention fusion detector that jointly models rPPG and facial behavior tokens. This mechanism explicitly captures the cross-level dependencies between pulse dynamics and facial motion to learn a robust, joint authenticity representation. Extensive experiments using a subject-disjoint 5-fold evaluation demonstrate the superiority of our approach. Achieving a 92.80% AUC on constructed datasets using face swapping and 96.78% AUC on motion transfer, our model outperforms both the rPPG-only single modality baseline and the best feature-level fusion methods. Furthermore, transfer-learning result of the fusion detector on Celeb-DF-v2 while keeping both feature extractors fixed achieves 91.20% accuracy and 86.08% AUC, which suggests applicability under target-domain adaptation.

Chenxi Yang, Yassine Ouzar, Larbi Boubchir

LIASD Laboratory Shanghai Jiaotong University Shanghai, China · LIASD Laboratory University of Paris 8 Saint-Denis, France

Remote PhotoplethysmographyDeepfake DetectionCross-Modal Feature MatchingMultimodal Fusion
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Sep 11, 2026

RA-SOD: Reliability-Aware RGB-T Salient Object Detection under Modality Degradation

RGB-Thermal (RGB-T) salient object detection leverages complementary cues from visible and thermal modalities to improve robustness in challenging environments. However, in real-world scenarios, the reliability of each modality is inherently unstable: RGB images degrade under low illumination, motion blur, and noise, while thermal imagery often suffers from contrast compression and sensor artifacts. Such degradation introduces unreliable perceptual evidence that can mislead cross-modal fusion and significantly deteriorate detection performance. To address this challenge, we propose RA-SOD, a reliability-aware RGB-T salient object detection framework that explicitly models modality reliability and integrates it into feature learning and cross-modal fusion. First, we introduce a reliability-conditioned representation that adaptively compensates degraded modality features while preserving structural cues. Second, an uncertainty-guided dual-stream refinement strategy progressively corrects cross-modal representations while suppressing unreliable evidence. Finally, we propose a pixel-wise modality competition mechanism that dynamically selects modality cues according to spatial reliability for fine-grained fusion. Extensive experiments on four benchmarks (VT821, VT1000, VT5000, and VT-IMAG) demonstrate that RA-SOD achieves state-of-the-art performance and exhibits strong robustness under severe modality degradation. Code and models are available at https://github.com/zaoxienian/RA-SOD.

Hongbo Gao, Zhengyu Li, Xueru Nie +4

State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, No. 92 West Dazhi Street, Nangang District, Harbin, Heilongjiang 150006, China · School of Computer Science, University of Sydney, Camperdown, Sydney, NSW 2006, Australia

Multimodal RobustnessSalient Object DetectionMultimodal Fusion
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Sep 11, 2026

RoES: Rotational Equivariant Selective-frequency Fusion for Multimodal Images

Infrared-visible image fusion facilitates robust multimodal perception by integrating complementary textural nuances from visible sensors with thermal signatures from infrared systems. Due to the task's inherently ill-posed nature, existing methods heavily rely on structural priors but typically enforce rotation equivariance uniformly across all features. Such a holistic approach overlooks a critical distinction where low-frequency shared structures strictly adhere to equivariant constraints while high-frequency modality-specific details require greater flexibility to preserve unique information. To bridge this gap, we propose RoES, a Rotational Equivariant Selective-frequency fusion network. Instead of employing static decomposition, we introduce a trainable rotation-enhanced updater/predictor module to dynamically decouple low- and high-frequency components. The resulting representations are then processed through a dual-branch fusion module tailored for spectral consistency. Specifically, a rotation-equivariant Mamba is employed to capture long-range structural dependencies in the low-frequency domain, while a polar spectral attention-based Dual-Fourier block refines high-frequency details under explicit low-frequency guidance. Extensive experiments demonstrate that RoES consistently achieves state-of-the-art performance in both fusion quality and downstream object detection, establishing a robust solution for multimodal fusion by reconciling frequency-selective features with equivariant constraints. The source code is available at https://github.com/BryceLosky/RoES-Fusion.

Jiabao Wang, Wenjian Liu, Yaoming Cai +5

Zhongnan University of Economics and Law Wuhan, Hubei, China · City University of Macau Macau, China · University of Illinois Chicago Chicago, Illinois, United States +3

Multi-Source Image FusionInfrared-Visible Image FusionEquivariant Neural NetworksFrequency-Domain Image ProcessingMultimodal Fusion
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Sep 10, 2026

Reading the Whole Heart: Latent-Attention Masked Autoencoders for Multimodal Cardiac Representation Learning

Cardiovascular diagnosis and treatment rest on integrating complementary modalities, such as electrocardiogram, echocardiography, and chest X-rays, each capturing distinct but complementary aspects of cardiac pathophysiology. Yet most medical foundation models remain modality-specific, combining modalities only for finetuning or post-training. This discards the cross-modal evidence clinicians naturally integrate and ignores the structure within each modality. We introduce Latent-Attention Masked Autoencoder (LAMAE), a multimodal, structure-aware masked autoencoder that jointly learns patient-level representations during self-supervised pretraining. Instead of fusing modalities post hoc, LAMAE exchanges information directly in the latent space through a shared latent-attention module operating over a study-view-entity hierarchy, enabling aggregation of variable observations and handling of missing modalities. Pretrained on over 500'000 MIMIC-IV hospital stays, LAMAE outperforms modality-specific pretraining and strong contrastive and vision-language baselines across multimodal hospital-stay tasks, such as in-hospital mortality, ICD-10 and DRG coding, and length of stay, while remaining competitive on unimodal tasks. Modeling this structure also pays off within a single modality: even without cross-modal information, the latent-attention module improves representations over modality-specific pretraining.

Andrea Agostini, Simon Böhi, Moritz Vandenhirtz +7

Department of Computer Science, ETH Zurich, Switzerland · Department of Biomedical Engineering, University of Basel, Switzerland

Masked AutoencodersHealthcareMultimodal PretrainingMultimodal FusionMultimodal Learning
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Sep 10, 2026

Enabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework

Multimodal stroke recurrence prediction requires effective integration of heterogeneous clinical and imaging data, yet modality imbalance often causes models to over-rely on dominant modalities and underutilize complementary information. While self-supervised pretraining and selective parameter freezing are commonly employed to improve representation learning and fine-tuning stability, their effect on modality contributions and cross-modal behavior in multimodal medical models remains largely unexplored. In this work, we investigate whether image pretraining on 3D CTA scans reduces modality imbalance and improves cross-modal integration for stroke recurrence prediction, a clinically critical task we recently addressed. To this end, two multimodal neural networks are pretrained in a self-supervised manner and subsequently fine-tuned using two distinct freezing strategies. Their performance and modality utilization are compared against both the baseline model from our previous work and models trained entirely from scratch in this study. Our results demonstrate that self-supervised pretraining enables more effective utilization of the multimodal image-tabular dataset, outperforming both the prior baseline and all non-pretrained models. Notably, the best-performing Vision Transformer based neural network successfully overcomes unimodal collapse. Synergy analysis reveals significant interactions between vision and both gender and CHD, suggesting clinically relevant patterns for stroke recurrence. Overall, our findings demonstrate that self-supervised pretraining and strategic fine-tuning support more balanced modality utilization and enable meaningful cross-modal interactions. Code is publicly available at https://github.com/ChristianGappGit/SSL_Pretraining.

Christian Gapp, Elias Tappeiner, Martin Welk +8

Institute of Biomedical Image Analysis UMIT TIROL – Private University for Health Sciences and Health Technology, Eduard-Wallnöfer-Zentrum 1, 6060 Hall in Tirol, Austria · VASCage – Centre on Clinical Stroke Research, 6020 Innsbruck, Austria · Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria +2

Multimodal LearningMultimodal FusionClinical Outcome PredictionMultimodal PretrainingSelf-Supervised Pre-Training
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Sep 10, 2026

RAMamba-Net: A Reliability-Aware and Mamba-Based Multimodal Fusion Network for Auditory Attention Detection

Auditory attention decoding (AAD) identifies the attended speaker from physiological signals, supporting neuro-steered hearing devices and natural human-machine interaction. Electroencephalography (EEG) is the dominant modality for AAD but provides incomplete evidence in naturalistic audio-visual scenes, motivating EEG and electrooculography (EOG) fusion. Existing approaches remain limited by weak cross-modal interaction, inefficient temporal modeling, and low robustness to sample variations. To address the limitations, we propose RAMamba-Net, a reliability-aware Mamba-based multimodal fusion network for AAD. RAMamba-Net employs a Mamba-enhanced band-aware convolutional Transformer to capture band-specific EEG patterns and long-range temporal dynamics. A dual-branch temporal-spatial encoder models EOG temporal and inter-channel dependencies. Cross-modal attention enables explicit modality interaction. Then, a reliability-aware module is introduced to estimate sample-wise modality weights for feature and prediction consistency, thereby enhancing multimodal fusion. Experiments on two AAD benchmarks demonstrate that RAMamba-Net effectively exploits complementary EEG-EOG information, yielding accuracy gains of 5.76% over unimodal baselines, together with more robust decoding and discriminative representations. Further analyses show that explicit cross-modal interaction improves multimodal alignment, while the reliability-aware module suppresses unreliable modality evidence and is robust to signal perturbation and parameter variation.

Xingyi He, Ziwei Wang, Dongrui Wu

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, China

Neural DecodingEEG DecodingMultimodal FusionReliability-Aware Multimodal Fusion
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Sep 10, 2026

The Illusion of Balanced Multimodal Sentiment Analysis: Beyond the Limits of Optimization-Based Methods

Multimodal Sentiment Analysis (MSA) remains constrained by modality imbalance, yet the field continues to rely on optimization-based balancing methods that promise more than they deliver. We provide three contributions: 1) a unified evaluation framework testing gradient and loss-based balancing strategies under controlled settings; 2) a theoretical diagnosis explaining why these methods fail, as they conflate fitting speed with discriminative contribution; and 3) a research agenda toward held-out discriminative modality valuation. Experiments on CMU-MOSI and CMU-MOSEI reveal three shortcomings: no strategy reliably outperforms Late Concatenation; performance is sensitive to hyperparameters; and even ratio calibration fails to yield consistent gains. The core issue is fundamental: loss is not utility, and gradients are not importance. Modality imbalance remains unresolved, motivating utility estimation from held-out performance.

Ioanna Kaffeza, Efthymios Georgiou, Alexandros Potamianos

PERSEE Center, Mines Paris-PSL University, Sophia-Antipolis, France · National Technical University of Athens, Athens, Greece · University of Bern, Bern, Switzerland +2

Modality ImbalanceMultimodal OptimizationMultimodal Sentiment AnalysisMultimodal FusionMultimodal Model Evaluation
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Sep 10, 2026

Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models

Short-term photovoltaic (PV) power and global horizontal irradiance (GHI) forecasts are essential for effective dispatch, reserve scheduling, and grid operations. At these forecasting horizons, errors are predominantly driven by cloud induced ramps: relying solely on historical numerical data may struggle to anticipate an incoming cloud, making ground-based sky images a crucial complementary physical signal. Furthermore, forecast performance is highly sensitive to location and local observing conditions, creating a strong need for site-specific data that are often scarce. Recently, large language models (LLMs) have demonstrated competitive performance and high data efficiency in time-series forecasting. Despite their success, existing LLM-based forecasting methods remain predominantly unimodal, relying primarily on historical numerical time-series data. Effectively incorporating sky imagery into an LLM-based forecasting framework remains under-explored and an open challenge. In this paper, we propose SolCloudLLM, an LLM-based multimodal forecasting framework. SolCloudLLM aligns sky-image patches with time-series patches and fuses their corresponding representations through bidirectional multimodal fusion, yielding a unified representation that is subsequently mapped into the embedding space of an LLM. Extensive experiments on the SIRTA and SKIPP'D datasets demonstrate that SolCloudLLM consistently outperforms the best baseline methods in MSE across all forecasting horizons, achieving a maximum relative MSE reduction of 25.4%. Stratified analysis further indicates that the benefits of multimodal fusion are concentrated primarily under cloudy conditions. Notably, SolCloudLLM achieves the best performance in nearly all few-shot settings, whereas other deep learning baselines experience substantial performance degradation and are frequently outperformed by the non-learning physical method.

Ken Chen, Maneesha Perera, Wei Wang +3

Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia · Department of Mechanical Engineering, Sri Lanka Institute of Information Technology, Sri Lanka

Multivariate Time Series ForecastingMultimodal Large Language ModelsLLM-Based Time Series ForecastingSolar Power ForecastingMultimodal FusionSolar Irradiance Forecasting
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Sep 10, 2026

Toward Interpretable Multimodal Fusion: Heat Conduction Modeling for Hyperspectral and LiDAR Joint Classification

The fusion of hyperspectral (HS) and Light Detection and Ranging (LiDAR) data plays a crucial role in enhancing land-cover classification by jointly exploiting spectral, spatial, and structural cues. However, existing multimodal fusion methods still struggle to model long-range dependencies and complex anisotropic interactions while maintaining computational efficiency. This paper introduces M2Heat, a physics-inspired framework that investigates multimodal fusion through the lens of heat conduction. At its core, a physics-driven visual heat conduction module (vHeat) and enhanced Frequency Value Embeddings (FVEs) simulate anisotropic information flow, enabling the capture of global dependencies with sub-quadratic complexity and physical interpretability. This mechanism, combined with a hybrid spatial-frequency fusion strategy named Cross-Frequency Fusion (CFF) module, produces highly discriminative and robust feature representations. M2Heat achieves competitive overall performance on three benchmarks, i.e., Trento, Houston2013, and Augsburg, while providing an interpretable heat-conduction-guided perspective for multimodal feature fusion. These results indicate the potential of heat-conduction-guided neural operators for efficient and interpretable RS multimodal fusion. The source code is publicly available at https: /github.com/Weikan0425/M2Heat_HSI_LiDAR.

Kan Wei, Jiahui Cui, Jing Yao +3

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China · Key Laboratory of Target Cognition and Application Technology, Beijing 100190, China · School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China +3

Multimodal Model InterpretabilityHyperspectral Image ClassificationMultimodal FusionMultimodal Sensor Fusion
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Sep 9, 2026

TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs

The pedestrian crossing intention task involves predicting whether pedestrians are likely to cross the road from the point of view of an autonomous vehicle. We introduce TrajFusionNet+, a novel transformer-based model for pedestrian crossing intention prediction. TrajFusionNet+ combines sequential and visual representations of pedestrian trajectory with a graph-based representation of the scene context in order to predict pedestrian crossing intention. The proposed architecture builds upon our previous model, TrajFusionNet, and comprises three branches: a Sequence Attention Module (SAM), which processes a sequential representation of past and predicted pedestrian trajectories; a Visual Attention Module (VAM), which utilizes a visual representation of the pedestrian trajectories by overlaying observed and predicted bounding boxes onto scene images; and a Graph Attention Module (GAM), which extracts pedestrian-centric graphs from segmented scene images and captures the relational dependencies between pedestrians and traffic elements. TrajFusionNet+ achieves improved state-of-the-art performance on the two most widely used pedestrian crossing intention datasets, PIE and JAAD. Furthermore, we introduce a new evaluation protocol in which models are trained jointly on the PIE and JAAD datasets but evaluated separately on each. Under this setting, TrajFusionNet+ demonstrates superior generalization compared to existing approaches.

François G. Landry, Moulay A. Akhloufi

Perception, Robotics and Intelligent Machines Research Group (PRIME) Department of Computer Science, Universit´e de Moncton, Canada

Pedestrian Intention PredictionMultimodal Fusion
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Sep 9, 2026

RiVaT-Fuse: Reliability-Calibrated Variational Tensor Fusion with Matrix-Valued Trust for Multimodal Prediction under Modality Uncertainty

Multimodal prediction from images and structured metadata requires integrating complementary evidence whose reliability can vary across samples and latent directions. A single confidence weight per modality cannot capture this directional variation. We propose RiVaT-Fuse, a reliability-calibrated variational tensor fusion framework that formulates fusion as sample-wise latent-state estimation. For each image-metadata pair, the fused representation minimizes a quadratic objective combining agreement with modality embeddings, structured cross-modal interactions, and regularization. Positive-definite, low-rank-plus-diagonal trust matrices are conditioned on learned state descriptors and metadata completeness, allowing modality contributions to vary across latent directions. Additive, multiplicative, and relational interactions model cross-modal dependencies within the latent estimation objective. The resulting system admits a unique solution computed through a differentiable linear solve. A first-order analysis with fixed trust operators relates latent sensitivity to system conditioning and perturbations in modality embeddings and interactions. The framework further incorporates a state-binned entropic surrogate for conditional distributionally robust learning and task-coupled quadratic prediction heads. We instantiate RiVaT-Fuse on mBRSET, pairing retinal images with clinical and demographic metadata for diabetic retinopathy grading, diabetic macular edema detection, and referable-status prediction. Comparisons with unimodal and representation-level fusion baselines assess the predictive utility of the complete framework across these related clinical tasks.

Yingfan Xu, Tieming Liu, Ye Liang

School of Industrial Engineering and Management Oklahoma State University · Department of Statistics, Oklahoma State University

Multimodal RobustnessUncertainty-Aware Multimodal FusionMultimodal FusionReliability-Aware Multimodal Fusion
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Sep 9, 2026

OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with HIPAA and GDPR can constrain centralized aggregation of sensitive patient data. This leaves a crucial void of secure fusion of visual and textual context across distant networks. Thus, we present OmniMed-FL, a controlled systems study of multimodal federated learning for five-class clinical condition classification (Normal, Pneumonia, COVID-19, Pleural Effusion, Cardiomegaly). Our proxy corpus pairs 3,000 public chest radiographs with 3,000 class-conditioned synthetic notes, matched by class, not by patient. The framework benchmarks eight fusion strategies, three initializations, four missing-text imputation rules, and matched federated baselines under non-IID Dirichlet partitioning across 3 to 20 hospital clients. As all notes are synthetic and pairing is not patient-level, these are descriptive proxy comparisons, not estimates of diagnostic performance or deployment readiness. Within those limits with clients (K=5K=5) and severe skew (α=0.1α=0.1), local-only training achieves a macro-F1 score of 0.297, FedAvg achieves 0.662±0.0740.662\pm0.074, FedProx 0.737±0.0850.737\pm0.085, a matched FedMME-style one-shot ensemble 0.647±0.0800.647\pm0.080, and our SCAFFOLD-AdamW adaptation 0.070±0.0150.070\pm0.015, the 0.075 FedProx-FedAvg gap falling inside the wider of the two two-seed standard deviations. Over a 4×34\times3 grid, label skew costs up to 0.27 F1 whereas a near-sevenfold client increase costs at most 0.10, while bidirectional volume grows linearly to 183.5 GiB at K=20K=20. Multimodal fusion leads on both corpora, scoring 0.956 against 0.934 for text and 0.664 for images on the synthetic corpus and 0.906 against 0.880 and 0.737 on the radiograph corpus, for 2.3×2.3\times the model state of text alone.

Ayush Debnath, Ruelia Saha, Sudip Misra

Indian Institute of Technology Kharagpur, India · KTH Royal Institute of Technology, Sweden · SRM University-AP, India +1

Multimodal Federated LearningMedical DiagnosisNon-IID Federated LearningMultimodal ClassificationMultimodal Fusion
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Sep 9, 2026

A Confidence-Aware Multimodal Fusion Framework for Industrial Human-Robot Collaboration

A confidence-aware multimodal fusion framework (CAMF) is proposed to realize reliable human intention prediction for industrial human-robot collaboration. This framework fuses four heterogeneous modalities including object 6D pose, gaze, skeletal motion and IMU-based hand motion. It embeds a confidence-trend-driven dynamic fusion mechanism into BiLSTM to adaptively balance bidirectional temporal features according to real-time modality reliability. A confidence-guided balanced learning strategy combined with a confidence freezing mechanism is further adopted to adjust network gradients dynamically, suppress noise from low-quality modalities and mitigate cross-modal learning bias. A physical platform based on the UR3 collaborative robot is built for experimental validation. Comparative results show that the proposed method reaches an intention recognition accuracy of 91.86% and outperforms existing multimodal fusion approaches in overall performance and stability. It also maintains satisfactory accuracy under low light and partial occlusion interference. In practical assembly tasks, the framework enables proactive and stable human-robot cooperation with strong environmental adaptability.

Xinyu Liu, Qiqi Dong, Boya Jia +2

Intent ClassificationHuman-Robot CollaborationUncertainty-Aware Multimodal FusionMultimodal FusionReliability-Aware Multimodal Fusion
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Sep 9, 2026

When Fusion Fails: Corruption-Aware Rebalanced Fusion for Multi-Modal Medical Image Segmentation

Multi-modal medical image segmentation leverages complementary diagnostic information, yet fusion can underperform single-modality baselines when spatially aligned inputs differ in quality. Here, "corruption" primarily denotes resolution-induced degradation rather than misalignment or complete modality absence, while synthetic noise is evaluated only as an auxiliary setting. We identify a critical optimization-inference inconsistency: degraded modalities can receive weak training updates yet substantially affect predictions, indicating active interference with fusion. We attribute this failure to resampling-induced feature corruption and optimization bias, where noisy features propagate through skip connections and encourage unreliable modality selection. We therefore propose CoReFuse-Med, a Corruption-aware Rebalanced Fusion framework that suppresses corruption during feature transmission and rebalances modality contributions during high-level fusion. Experiments on EPVS, BraTS, and WMH, including multiple Z-axis slice-retention ratios and an auxiliary noise test, demonstrate improved accuracy and robustness under modality-quality discrepancies. Our code is available at https://github.com/lrever/CoReFuse.

Yuchen Pei, Xiaoyu Hu, Yixiong Zou +5

Central China Normal University Wuhan, China · Huazhong University of Science and Technology Wuhan, China · Guangzhou University of Chinese Medicine Guangzhou, China +2

Cross-Modality Medical Image SegmentationMulti-Source Image FusionMultimodal RobustnessMedical Image SegmentationMultimodal Fusion
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Sep 9, 2026

Multimodal Emotion Recognition in Conversations via Class-Wise Adaptive Modality Fusion and Affective Geometry

Emotion Recognition in Conversations (ERC) requires integrating heterogeneous textual, audio, and visual cues while accounting for conversational context and emotional dynamics. We extend the Self-Distillation Transformer architecture for ERC with appearance+geometry visual representations, class-wise adaptive modality fusion, and a valence-arousal prior for affective transitions. On the MELD and IEMOCAP datasets, geometry-enhanced visual representations improve weighted F1 by 0.27 and 4.36 points over appearance-only features, respectively, while class-wise adaptive fusion provides further gains of 0.17 and 0.25 points over the original softmax gate. The valence-arousal prior yields targeted improvements of 0.30 and 0.74 accuracy points on emotionally shifted utterances while preserving performance on stable turns. These results indicate that structured facial cues, emotion-dependent modality weighting, and affective geometry provide complementary benefits for multimodal ERC.

Oriol Marín, Roger Marí, Gloria Haro +1

Eurecat, Centre Tecnològic de Catalunya, Barcelona, Spain · Universitat Pompeu Fabra, Barcelona, Spain · Serra Húnter Fellow Programme, Universitat Pompeu Fabra, Barcelona, Spain

Emotion Recognition in ConversationsMultimodal Emotion RecognitionMultimodal Fusion
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Sep 9, 2026

EEGBind: Detecting Source-Level Interictal Epileptiform Discharges via EEG-Centric Multimodal Binding

Source-level analysis of interictal epileptiform discharges (IEDs) is relevant to presurgical evaluation and treatment planning because it helps characterize where epileptiform activity is likely to arise. Beyond detecting whether an IED is present, this setting requires assigning IED-positive activity to clinically meaningful brain-region categories. This setting is challenging because source-region evidence in short electroencephalography (EEG) windows can be subtle, partial, and affected by subject variability, class imbalance, and imperfect multimodal context. We present EEGBind, an EEG-centric multimodal binding framework for five-class source-level IED classification. EEGBind treats EEG as the primary modality and binds synchronized video-context features around an EEG-centric representation. Instead of relying on early or overly strong multimodal fusion, which may perturb the source-sensitive EEG representation, EEGBind uses video context as auxiliary evidence for robust classification. A view-consistent repair stage is further used to improve hidden-set robustness while preserving the learned source-class boundary. On the NeuroMM 2026 Grand Challenge Track 3 NMM-Source-IED benchmark, EEGBind achieves 0.8395 on weighted-F1 and outperforms strong competitors. These results support EEG-centric multimodal binding as a practical strategy for source-level IED classification. The open-source code is available at https://github.com/HKUSTGZ-ML4Health-Lab/NeuroMM2026_IED_Detection.

Muchen Li, Anglin Liu, Xuetian Gao +2

Artificial Intelligence Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China · Ringgee Smart Technologies Co., Ltd. Fuzhou, Fujian, China

ElectroencephalographyMultimodal FusionMultimodal Learning
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Sep 8, 2026

VeriScene: Reconstructing Crime Scenes from Legal Evidence via World-Model Agent

World models take multimodal inputs like text, photos, and diagrams to generate dynamic scenes in accordance with the laws of physics, thus opening a compelling application: fusing multimodal legal evidence to re-create a crime scene and re-enact how an offence could have been committed. However, feeding the raw, unorganized evidence into a world model fails in forensic use: it silently drops evidence, glosses over contradictory testimony, and produces motion that violates the evidentiary record. This paper presents VeriScene, an agent that orchestrates the world model: it reconstructs crime scenes from forensic photographs and witness statements of varying reliability, keeping every claim traceable to evidence and every motion physically plausible. VeriScene iteratively fuses the evidence into a cited narrative under an auditing loop, verifies the hypothesized dynamics via probe rollouts in the world model with corrective constraint injection, and renders the offence as a re-enactment video from a fused keyframe. On a benchmark of 25 crime scenarios across 7 physically-driven case types (139 forensic-style photographs and 65 statements with planted unreliability), VeriScene attains 0.9014 evidence coverage and 0.7217 factual consistency (0-1 scale) on the 20 test scenes, outperforming an end-to-end multimodal-LLM baseline by 20.35% in factual consistency and 34.88% in temporal coherence, while generalizing across four LLM orchestration backends at USD 1.82 per scene.

Kevin Chuanpu Fu, Yongsen Zheng, Zee Kin Yeong +1

Nanyang Technological University, Singapore · Singapore Academy of Law, Singapore

Physical Consistency in Video GenerationDigital ForensicsMultimodal Fusion
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Sep 7, 2026

Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data

Deep learning is an efficient technique to monitor the real time welding process, reducing post-welding repairs and production delays. This paper leverages the monitoring capability by proposing a multi modal temporal attention based deep learning defect detection model for internal defects that are challenging to detect, including porosity, lack of penetration and fusion, undercut, and cold lap during Gas Metal Arc Welding in fillet joints. The model is trained on collected welding images and sound data from an industrial collaborative welding robot. The results show that the attention module can improve the F1 Score to 0.99. We use explainable Artificial Intelligence to interpret the proposed models behavior and dataset distribution, determining potential important areas in image and sound spectrograms and preferred modality to detect each defect. This improves trust and reliability in Artificial Intelligence driven welding inspection.

Mobina Mobaraki, Mahyar Asadi, Klaske Van Heusden +1

University of British Columbia- Vancouver campus, 2329 West Mall, Vancouver, V6T 1Z4, British Columbia, Canada · Novarc Technologies Inc., 1225 E Keith Rd, North Vancouver, V7J 1J3, British Columbia, Canada · University of British Columbia, Kelowna campus, 3333 University Way, Kelowna, V1V 1V7, British Columbia, Canada

ManufacturingIndustrial InspectionMultimodal Fusion
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Sep 7, 2026

Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection

The growing realism and accessibility of manipulated and generated faces threaten the trustworthiness of digital media. To detect such forgeries, deepfake detectors based on vision foundation models have shown promising performance, but they typically rely on a single pretrained representation and are prone to overfitting to particular training distributions. To improve generalization to unseen forgeries, we propose UCF-Net, an uncertainty-aware cascaded fusion network that harnesses CLIP's language-aligned semantic priors and DINO's self-supervised visual-structure priors. UCF-Net extracts hierarchical features across Transformer depths, uses layer-wise expert aggregation to adaptively combine each encoder's multi-level cues, and performs weighted fusion of the resulting representations based on entropy-derived uncertainty. We further consolidate public deepfake datasets into a unified benchmark of approximately 4M images and construct a separate cross-generator evaluation set with over 8K face images from eight recent generators. On the unified benchmark, UCF-Net achieves the best mean AUC among the evaluated methods in both in-domain and cross-domain evaluations. On the cross-generator set, it adapts effectively with limited target-domain data, although zero-shot transfer remains challenging.

Xuechao Zou, Yi Zhou, Kai Li +4

Beijing Jiaotong University · Tsinghua University · Ant Group

Deepfake DetectionMultimodal Fusion
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Sep 7, 2026

Where Should Language Sit in a Multimodal Model? Lessons from What Language Does to Human Perception and Cognition

Language models compute over tokens: language is their input, their output, and increasingly their internal representation. Whether language should keep all of these positions depends on what language does to the system that uses it. The one system with a century of data on that question is the human. We review what language does to human perception, the brain, and thought, and read the same evidence against multimodal models and language models. Throughout, we treat language as a compressor that runs on a shared codebook: a word is an index, the content is in the receiver, and a community maintains the codebook. In humans the compression is measurable, learning the codebook reorganizes the senses, and thought survives the loss of language. We then measure the rule that models apply when two cues disagree, with cue-conflict experiments on six vision-language models and two robot policies. Surviving cues are weighted in the order their reliabilities prescribe, at 11 to 82% of the ideal observer's slope, and many answers copy the text. One policy family drops a cue that adds no information beyond the others rather than down-weighting it, another keeps it at a weight that fails when the cues conflict, and a visual cue that identifies the task in every training frame is never learned, because the language pathway already fits the data. Language models are the best current models of the human language network, and they have entered the human speech community, shifting word frequencies while alignment narrows their conceptual diversity. We close with seven implications for token-based systems. Language belongs at a model's boundary and in the shared codebook, as in the brain, not as its internal representation; the price of leaving the codebook inside is auditability.

Peng Xie, Amr Alanwar

TUM School of Computation, Information and Technology, Department of Computer Engineering, Technical University of Munich

Unified Multimodal ModelsMultimodal FusionMultimodal Model Evaluation
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Sep 7, 2026

RAFM-SER++: A Lightweight Multimodal Emotion Recognition Framework for Real-Time Behavioral Monitoring in Surveillance Systems

Recent multimodal Speech Emotion Recognition (SER) systems achieve high accuracy through interaction-heavy cross-modal transformers, but their computational cost limits deployment in latency-sensitive and resource-constrained surveillance systems. To address this challenge, we propose RAFM_SER++, a lightweight multimodal SER framework featuring an asymmetric Residual Attention Fusion Mechanism (RAFM). Rather than relying on computationally expensive bidirectional interactions, RAFM injects affective speech cues into semantic text representations through a one-directional residual attention pathway. Combined with a BYOL-inspired cross-modal alignment objective and attention-guided pooling, the proposed framework improves multimodal representation learning while maintaining low computational overhead. Experiments on the IEMOCAP and ESD benchmarks demonstrate that RAFM_SER++ consistently outperforms the HuBERT-Base baseline and achieves a superior accuracy-efficiency trade-off compared with the state-of-the-art MemoCMT. Specifically, RAFM_SER++ reduces trainable parameters by more than 60%, achieves faster inference (79.60 it/s), and attains BACC scores of 81.10% on IEMOCAP and 95.39% on ESD. These results indicate that lightweight asymmetric multimodal fusion is an effective alternative to interaction-heavy cross-modal transformers for real-time surveillance applications.

Ngo Truong Dinh, Tung-Lam Bui, Chi-Trung Duong +3

Faculty of Information Technology University of Science, VNU-HCM Ho Chi Minh City, Vietnam

Efficient Multimodal InferenceMultimodal Emotion RecognitionSpeech Emotion RecognitionMultimodal Fusion
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Sep 7, 2026

Fine-Grained Visual Preprocessing and Dual-Stream Temporal Modeling for Multimodal Sentiment Analysis on Social Media

Multimodal sentiment analysis often remains text-dominant due to raw-video noise and insufficient temporal modeling. Using CH-SIMS v2.0S, this study proposes three improvements: the NAPS pipeline---a seven-stage system integrating face tracking,identity embedding, and normalized lip-motion analysis to reduce visual noise;DS-TANet, combining an EfficientNetB2 static stream, RAFT optical-flow motion stream, motion-guided attention, and Bi-GRU temporal modeling; and DS-TAFNet, fusing visual and MacBERT-Base textual representations via concatenation fusion. With NAPS, the static visual baseline achieves 80.98% Macro F1, comparable to the text baseline of 80.55%; DS-TANet improves visual Macro F1 to 82.58%;and DS-TAFNet achieves 87.49% accuracy and 87.48% Macro F1. These results demonstrate that improving visual input quality and temporal representation is more effective than increasing fusion complexity under limited-data conditions.

Su Li, Yigong Zhang, Lei Xiong +1

School of Information Engineering, Kunming University, No. 2 Puxin Road, Guandu District, Yangpu Campus, Kunming, 650214, Yunnan, China

Sentiment AnalysisMultimodal Sentiment AnalysisMultimodal FusionTemporal Video Understanding
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Sep 7, 2026

NeuCME: Toward Dynamic Multimodal Continual Learning via Neural Combinatorics of Multiple Experts

Multimodal continual learning has recently shown great potential for developing agents with human-like intelligence by continuously learning new tasks across multiple modalities. However, existing methods typically assume that the set of modalities per task is predefined and fixed. In this paper, we investigate a more realistic learning setting, referred to as dynamic multimodal continual learning, in which the set of modalities may vary across tasks rather than remaining fixed. This setting involves two primary challenges: (i) spatio-temporal catastrophic forgetting and (ii) adaptive multimodal fusion. To address these challenges, we propose NeuCME (as shorthand for Neural Combinatorics of Multiple Experts), a novel framework designed to effectively learn and integrate knowledge across tasks with varying modalities. The proposed NeuCME model comprises three key components, namely modality-combinational rehearsal, multi-gated mixture-of-experts, and task relevance-guided distillation. Furthermore, we formulate an evaluation metric to quantify the dynamism of task sequences and then set up a comprehensive benchmark with different degrees of dynamism. Extensive experiments using four real-world datasets demonstrate that the proposed NeuCME outperforms state-of-the-art methods markedly.

Kai Guo, Chuanbin Liu, Peng Hu +2

College of Computer Science, Sichuan University, Chengdu, China · School of Economics and Management, China University of Petroleum (Beijing), Beijing, China · School of Artificial Intelligence, Sichuan University, Chengdu, China +1

Multimodal LearningCatastrophic ForgettingMultimodal Fusion
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Sep 6, 2026

LASSNet: Level-Aware Availability-Conditioned Spatial-Semantic Fusion for Brain Tumor Segmentation with Missing MRI Modalities

Brain tumor segmentation from multimodal MRI relies on complementary evidence across four imaging sequences, yet one or more modalities may be unavailable because of acquisition cost, protocol variation, scan failure, or patient condition. Existing work has explored reconstruction, knowledge transfer, and direct feature fusion, but leaves open whether missing-modality fusion should change with representation level. High-resolution lateral features retain spatial detail, whereas compressed bottleneck features encode semantic and inter-modality context. We therefore hypothesize that fusion should be conditioned jointly on modality availability and feature hierarchy. We propose the Level-Aware Availability-Conditioned Spatial-Semantic Fusion Network (LASSNet), which contains two level-specialized modules. Hierarchical Availability-Conditioned Fusion (HACF) constructs four lateral representations using count-normalized aggregation of available modalities, mask-conditioned channel modulation, and local 3D refinement. Tri-Scale Relational-Spatial Fusion (TriRSF) models relations among available modality descriptors and spatial context across multiple bottleneck resolutions, followed by cross-scale aggregation and availability-conditioned global spatial attention. A shared coarse-to-fine decoder starts from TriRSF semantics and progressively injects HACF features, without reconstructing missing inputs. Across all 15 non-empty modality configurations, LASSNet obtains mean Dice scores of 76.7% and 83.2% over WT, TC, and ET on BraTS2019 and BraTS2023, respectively.

Haobin Chen, Ao Chang, Rundong Wang +3

School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, Anhui, 230026, China · Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE) Lab, YRD-RIGHT, Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, 215123, China · College of Information Science and Technology, Donghua University

Missing-Modality Medical Image SegmentationMedical Image SegmentationBrain Tumor Segmentation3D Medical Image SegmentationMultimodal Fusion
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Sep 6, 2026

Vision-Guided Text Prompt Tuning for Multimodal Sentiment Analysis

Multimodal sentiment analysis requires effective modeling of both verbal semantics and non-verbal affective cues. A central challenge is to calibrate text-centered sentiment understanding with visual facial evidence in a controlled, adaptive, and parameter-efficient manner. Text usually serves as the semantic anchor, whereas visual cues provide complementary evidence for ambiguous or implicit expressions; however, indiscriminate fusion may introduce visual noise and distort textual semantics. Moreover, fully fine-tuning large visual and textual encoders is costly and prone to overfitting on limited and scenario-dependent MSA benchmarks. To address these issues, we propose Vision-Guided Text Prompt Tuning (VG-TPT), which formulates visual-text sentiment modeling as controllable visual calibration of frozen text representations. VG-TPT injects visual affective cues into a frozen BERT encoder through layer-wise adaptive prompts, rather than relying on late-stage feature fusion or full backbone tuning. A co-guided router composes prompts from a trainable prompt bank according to both the evolving text state and the visual guidance feature, enabling sample-specific and layer-specific modulation. Experiments on CMU-MOSEI and CMU-MOSI show that VG-TPT consistently improves over text-only baselines and achieves competitive or superior performance compared with several full-modality methods, while updating only 2.4M trainable parameters. The code is available at https://github.com/ma-tubu/VG-TPT.

Xiaoran Kou, Jingyi Wu, Peng Sun +2

College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China · College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai 200433, China · Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou 215316, China

Multimodal Sentiment AnalysisPrompt TuningMultimodal FusionParameter-Efficient Fine-Tuning
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Sep 5, 2026

MVFA: A Multi-View Text-Guided Multimodal Fusion LLM Adapter for Sentiment Analysis and Emotion Recognition

Multimodal sentiment analysis and emotion recognition in conversations demand effective modeling of heterogeneous interactions across textual, acoustic, and visual modalities. Although large language models (LLMs) offer powerful language understanding, adapting them to multimodal affective computing remains challenging: full-model fine-tuning is computationally prohibitive, while many existing lightweight adapters fail to preserve rich textual cues during cross-modal fusion. To address these limitations, we propose the multi-view text-guided multimodal fusion adapter (MVFA), a parameter-efficient framework that augments frozen LLMs with strong multimodal reasoning capability. MVFA first constructs complementary text views via max pooling, mean pooling, and attention pooling; these views then guide cross-modal interactions with audio and visual features. The fused multimodal representations are subsequently compressed into a compact set of learnable pseudo-tokens through an Enhanced Q-Former Fusion Module. Using ChatGLM3-6B-base as the primary backbone, we further validate MVFA on LLaMA2-7B and Qwen3-8B to examine its portability across multiple frozen LLM backbones. MVFA is evaluated on three challenging datasets: CH-SIMS V2.0, MELD, and CHERMA. Experimental results demonstrate that MVFA achieves state-of-the-art performance on key metrics while updating only a small fraction of parameters. Specifically, it attains 84.62% Acc2 and 84.59% F1 on CH-SIMS V2.0, 67.36% Acc and 66.03% WF1 on MELD, and 74.66% Acc on CHERMA. These findings establish multi-view text-guided fusion as an effective and scalable paradigm for parameter-efficient multimodal LLM adaptation in affective computing. The code is publicly available at https://github.com/Overwhelm1208/MVFA.

Pengfei Shao, Jisheng Dang, Jiawen Fang +7

School of Information Science and Technology, Guangdong University of Foreign Studies, Guangdong, China · School of Information Science and Engineering, Lanzhou University, Lanzhou, China · School of Computer Science and Engineering, Sun Yat-sen University, Guangdong, China +2

Multimodal Sentiment AnalysisMultimodal Emotion RecognitionMultimodal FusionMultimodal Large Language Models
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Sep 4, 2026

Real-World Multi-Modal and Longitudinal Lung Cancer Dataset

Multi-modal learning has demonstrated strong potential in medical applications by integrating heterogeneous data sources such as medical imaging, clinical records, and genomics to improve predictive performance and support clinical decision-making. However, advances in this area are often constrained by two key challenges: the limited availability of well-curated, ready-to-use datasets that accurately reflect real-world conditions, where medical data are frequently collected inconsistently and are often incomplete; and the inherent difficulty of integrating heterogeneous data modalities. In this work, we introduce a newly curated multi-center, multi-modal, and longitudinal dataset designed to support the evaluation of a wide range of learning pipelines under realistic conditions. The dataset comprises a total of 1,365 lung cancer patients and has three imaging modalities (whole-slide images, CT scans, and PET scans), structured clinical data, transcriptomic, and longitudinal follow-up and treatment information. For each imaging modality the dataset contains more than one instance. Moreover, the dataset exhibits substantial and non-uniform missingness across modalities, making it well-suited for studying robust multi-modal fusion strategies. We further provide both uni-modal and multi-modal benchmarks on the task of 12-month overall survival prediction, disease-specific survival, as well as longitudinal benchmark of hazard prediction under severe missing data. Our results show that, despite high levels of missingness, integrating complementary modalities consistently improves predictive performance over uni-modal approaches, highlighting the value of multi-modal fusion in realistic clinical settings. The dataset and benchmark code are available at https://github.com/ritacmendes/MMIST-LUNG.

Rita Cordeiro Mendes, Maria Rita Fonseca Verdelho, Carlos Santiago +1

Institute for Systems and Robotics, Instituto Superior Técnico, Portugal

Multimodal Survival AnalysisMultimodal FusionCancer Survival PredictionLearning with Missing Data
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Sep 4, 2026

CoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image Segmentation

Multi-modal medical images and clinical reports provide complementary anatomical, functional, and semantic information for medical image segmentation. Effectively exploiting these heterogeneous sources requires fine-grained cross-modal information fusion that preserves subtle spatial details while capturing semantic dependencies across modalities. Existing fusion approaches frequently rely on cross-attention, whose computational burden increases rapidly with spatial resolution, making dense cross-modal interaction difficult on high-resolution feature maps, particularly for volumetric medical images. In this work, we propose CoMLP, a cooperatively-gated MLP module for fine-grained cross-modal information fusion in medical image segmentation. CoMLP models cross-modal dependencies through cooperative cross-gating, built upon complementary regional and dilated MLP interactions, to capture local and global cross-modal dependencies. We further develop a multi-source fusion architecture in which CoMLP performs both inter-image fusion across imaging modalities and vision-language fusion between visual features and textual reports, enabling heterogeneous information to be integrated without relying on dense cross-attention. Extensive experiments on five medical segmentation benchmarks, covering 2D/3D images, clinical reports, multiple imaging modalities, and diverse anatomical regions, demonstrate consistent improvements over state-of-the-art multi-modal and language-guided segmentation methods. Ablation studies further show that fine-grained interaction at high spatial resolutions and complementary local-global fusion are critical to the performance gains. These results demonstrate the potential of MLP-based interaction as an effective alternative for fine-grained cross-modal information fusion in medical image segmentation.

Mingyuan Meng, Shuchang Ye, Mingjian Li +3

Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China · Zhongguancun Academy & Zhongguancun Institute of Artificial Intelligence, Beijing, China · School of Computer Science, the University of Sydney, Sydney, NSW, Australia +1

Cross-Modality Medical Image SegmentationMedical Image SegmentationMultimodal Fusion
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Sep 4, 2026

Enhancing Multimodal Emotion Recognition via Multi-Feature Encoding and Attention-Based Fusion

Multimodal emotion recognition has attracted growing interest due to its importance in human-computer interaction, remote education, and healthcare. This paper proposes a novel multimodal emotion recognition framework that integrates rich audio and visual feature extraction with an attention-based fusion strategy. For audio, we extract three complementary feature types: semantic embeddings from Wav2Vec2, MFCC features, and statistical acoustic descriptors such as pitch, energy, and rhythm. These are aligned and fused via a BiLSTM to capture temporal dependencies. For video, we propose a ResNet50-BiLSTM architecture that combines deep residual learning and sequential modeling to extract expressive spatiotemporal features from facial sequences. To enhance multimodal synergy, we introduce a feature-level fusion mechanism based on multi-head attention, allowing the model to adaptively weigh contributions across modalities. Experiments conducted on the MELD and IEMOCAP datasets demonstrate that our model significantly outperforms baselines in both accuracy and robustness. Furthermore, ablation studies show that the attention-based fusion strategy significantly improves performance in unbalanced data settings. Our findings suggest that the proposed framework effectively captures diverse emotional cues from speech and visual expressions, and offers a practical and generalizable approach for real-world multimodal emotion recognition tasks.

Xu Lin, Ke Wang, Hui Kang +1

College of Computer Science and Technology, Jilin University, Changchun 130012, China · Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China

Multimodal Emotion RecognitionMultimodal Fusion
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Sep 3, 2026

Occlusion-Robust Multimodal Emotion Recognition in VR via Fusion of Facial Images and EMG

Head-mounted displays (HMDs) fundamentally limit emotion recognition in virtual reality (VR): by occluding the upper face, they render conventional image-based facial expression analysis incomplete, particularly for applications requiring real-time affective assessment. We address this challenge by fusing lower-face video with facial electromyography (EMG) from the occluded upper face to classify seven emotional categories (six basic emotions plus neutral). We introduce a synchronized multimodal dataset from 20 participants, pairing lower-face video with seven-channel upper-face EMG elicited by validated emotion stimuli. Under subject-independent test, our proposed late-fusion architecture merging convolutional visual embeddings with RBF-kernel EMG representations achieves 51% macro-F1, outperforming both image-only (41%) and EMG-only (43%) baselines. These results demonstrate that upper-face EMG provides robust complementary information under HMD-induced visual occlusion and establish a foundation for multimodal emotion recognition in naturalistic VR environments. This approach facilitates affect-adaptive applications, including communication training and therapeutic interventions. The dataset will be shared upon request under an ethical-use agreement.

Birgit Nierula, Karam Tomotaki-Dawoud, Mert Akguel +5

Fraunhofer HHI, Berlin, Germany · Humboldt University of Berlin, Berlin, Germany

Multimodal Emotion RecognitionMultimodal FusionEmotion Recognition
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