Beyond Modality Fusion: Deep Ensembles for Multimodal Classification
In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks. When modality imbalance is pronounced, various regularization techniques have been proposed to balance the learning process and overcome the inferior performance of late-fusion networks. In contrast, this work demonstrates that multimodal data can be effectively classified without any explicit modality fusion, using deep ensembles of unimodal networks. We systematically compare deep ensembles to late-fusion networks at equal parameter count and show that ensembles consistently outperform state-of-the-art late-fusion methods designed to address modali...
| Authors | Ilya Burenko, Dmitry Vetrov |
|---|---|
| Primary category | cs.LG |
| Published | 6 July 2026 |
| Last updated at source | 7 July 2026 |
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