UASPL selects training samples by an evidential loss that couples label fit with adaptive uncertainty-weighted KL regularization, yielding an easy-to-hard preference and stronger average classification results than loss-only SPL.
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CDPR uses an intuition pathway for cross-modal consensus and a reasoning pathway for quantifying and mitigating inconsistencies to improve multimodal intent recognition.
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UASPL: Uncertainty-Aware Self-Paced Learning with Evidential Neural Networks
UASPL selects training samples by an evidential loss that couples label fit with adaptive uncertainty-weighted KL regularization, yielding an easy-to-hard preference and stronger average classification results than loss-only SPL.
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Mitigating Multimodal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition
CDPR uses an intuition pathway for cross-modal consensus and a reasoning pathway for quantifying and mitigating inconsistencies to improve multimodal intent recognition.