CONFER：面向多模态情感识别中规则校准弱监督的冲突感知证据协商框架
CONFER: Conflict-Aware Evidence Negotiation for Regime-Calibrated Weak Supervision in Multimodal Emotion Recognition
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中文总结 AI 辅助
本文提出CONFER框架，针对多模态情感识别中跨模态冲突与自我报告标签不可靠问题，通过图结构的冲突感知证据协商实现弱标签校准，在多数据集严格LOSO协议下取得具竞争力的情感识别准确率，提升了对弱标签损坏的鲁棒性。
中文摘要 AI 辅助
多模态情感识别常将自我报告标签视为可靠监督，却忽略自我报告的不可靠性及跨模态冲突。本文提出CONFER，一种用于弱监督多模态情感识别的基于图的冲突感知证据协商框架。CONFER将各模态专家表示为节点，节点包含预测置信度、基于边界的不确定性，以及通过历史折外表现与当前样本不确定性估计的运行时可靠性。不确定性感知兼容性与可靠性导向的非对称边权控制迭代消息传递协商，随后输出同伴支持的预测。冲突减少、剩余分歧及平均模态不确定性进一步构成三类规则——共识、分歧与模糊性，用于样本特定的弱标签校准。在AMIGOS、MAHNOB-HCI与DEAP数据集上，采用依赖受试者的10折及严格留一受试者（LOSO）协议评估CONFER。CONFER表现具有竞争力，在严格LOSO评估下，AMIGOS-V上准确率达0.873，MAHNOB-V上达0.854。进一步分析显示，高冲突样本上协商增益更大，且对弱标签损坏的鲁棒性提升，表明跨模态冲突可为定向模态协调与监督可靠性估计提供有用信息。
英文摘要
Multimodal emotion recognition often treats self-reported labels as reliable supervision while overlooking self-report unreliability and cross-modal conflict. We propose \textbf{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 \textbf{0.873} accuracy on AMIGOS-V and \textbf{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.
