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arXiv Machine Learning By Tillmann Rheude, Roland Eils, Benjamin Wild

Fusion or Confusion? Multimodal Complexity Is Not All You Need

Read the original on arXiv Machine Learning →

The paper reexamines the belief that more complex multimodal neural architectures lead to better performance. By reimplementing 19 influential multimodal methods across nine datasets with up to 23 modalities, the authors find that increased architectural complexity often causes confusion rather than effective fusion, and such models rarely surpass unimodal baselines or a simple baseline (SimBaMM). They also highlight methodological flaws in top-tier multimodal studies and call for a shift toward standardized evaluation and methodological rigor.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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