mlsanityData to 14 Oct 2026

CONFER: Conflict-Aware Evidence Negotiation for Regime-Calibrated Weak Supervision in Multimodal Emotion Recognition

arXiv 2608.07867 ↗First posted 8 Aug 2026cs.LGcited by 0

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Abstract (arXiv)

Multimodal emotion recognition often treats self-reported labels as reliable supervision while overlooking self-report unreliability and cross-modal conflict. We propose CONFER, a graph-based conflict-aware evidence negotiation framework for weakly supervised multimodal emotion recognition. CONFER represents each modality expert as a node with a predictive belief, boundary-based uncertainty, and runtime reliability estimated from historical out-of-fold performance and current-sample uncertainty. Uncertainty-aware compatibility and reliability-directed asymmetric edge weights govern iterative message-passing negotiation, followed by peer-supported prediction readout. Conflict reduction, residual disagreement, and mean modality uncertainty further characterize three regimes---Consensus, Dissent, and Ambiguity---for sample-specific weak-label calibration. We evaluate CONFER on AMIGOS, MAHNOB-HCI, and DEAP under subject-dependent 10-fold and strict leave-one-subject-out (LOSO) protocols. CONFER achieves competitive performance, reaching 0.873 accuracy on AMIGOS-V and 0.854 accuracy on MAHNOB-V under strict LOSO evaluation. Further analyses show larger negotiation gains on high-conflict samples and improved robustness to weak-label corruption, indicating that cross-modal conflict provides useful information for both directional modality coordination and supervision-reliability estimation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 8 Aug 2026.

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References · 22

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10 more
  1. 2021Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysiscited by 180
  2. 2022Trusted Multi-View Classification with Dynamic Evidential Fusioncited by 168
  3. 2021Trusted Multi-View Classificationcited by 94
  4. 2024FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusioncited by 68
  5. 2019M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cuescited by 56
  6. 2025I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Expertscited by 30
  7. 2022CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learningcited by 24
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