# CMAFNet

Judging how immersed a VR football viewer is at a given moment is hard from pictures alone, since video cannot tell watching from disengagement, and naive concatenation of four signals carries low-quality channel noise into the decision. CMAFNet treats video as a semantic hub, runs bidirectional cross-attention with audio, EEG and eye tracking, weights each modality per sample by real-time signal quality, and uses an ordinal regression loss to keep immersion levels ordered. On 86,400 samples cut from 720 sixty-second VR match clips, four-level classification reaches 81.6% overall accuracy and 0.274 mean absolute error, 3.4 points above and 13.8% below the strongest 2024 baseline, MT-AMW.

Judging how immersed a VR football viewer is at a given moment is hard from pictures alone, since video cannot tell watching from disengagement, and naive concatenation of four signals carries low-quality channel noise into the decision. CMAFNet treats video as a semantic hub, runs bidirectional cross-attention with audio, EEG and eye tracking, weights each modality per sample by real-time signal quality, and uses an ordinal regression loss to keep immersion levels ordered. On 86,400 samples cut from 720 sixty-second VR match clips, four-level classification reaches 81.6% overall accuracy and 0.274 mean absolute error, 3.4 points above and 13.8% below the strongest 2024 baseline, MT-AMW.