Tag
This paper proposes CONFER, a graph-based conflict-aware evidence negotiation framework for weakly supervised multimodal emotion recognition, addressing self-report unreliability and cross-modal conflict. It achieves competitive accuracy on AMIGOS, MAHNOB-HCI, and DEAP benchmarks.
ConfSleepNet is a conflict-aware evidential framework for reliable sleep stage classification using multi-modal data. It introduces hybrid category structures and a conflict-aware aggregation method to resolve inter-view conflicts, demonstrating effectiveness on sleep staging tasks.