DICCAE dynamically weights a confusion loss using measured inter-class overlap and reports 65.5% audio-visual top-1 on VGGSound, but the evaluation protocol uses test data during training.
Multimodal machine learning: A survey and tax- onomy
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Dynamic Inter-Class Confusion-Aware Encoder for Audio-Visual Fusion in Human Activity Recognition
DICCAE dynamically weights a confusion loss using measured inter-class overlap and reports 65.5% audio-visual top-1 on VGGSound, but the evaluation protocol uses test data during training.