Gated Multi-modal Fusion reaches 0.82 macro F1 on HARMES, beating the concatenation baseline of 0.76 by 6 points under leave-one-participant-out evaluation.
Neural Computing and Applications32(14), 10209–10228 (Jul 2020)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2representative citing papers
Multimodal 3D CNN model with GMU, gated self-attention, and sparsely gated MoE achieves up to 95.47% accuracy on NC vs AD using MRI and PET, with ablations showing MoE benefit.
citing papers explorer
-
A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset
Gated Multi-modal Fusion reaches 0.82 macro F1 on HARMES, beating the concatenation baseline of 0.76 by 6 points under leave-one-participant-out evaluation.
-
Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET
Multimodal 3D CNN model with GMU, gated self-attention, and sparsely gated MoE achieves up to 95.47% accuracy on NC vs AD using MRI and PET, with ablations showing MoE benefit.