Simplicity in Multimodal Learning: A Challenge to Complexity
Published:Dec 28, 2025 16:20
•1 min read
•ArXiv
Analysis
This paper challenges the trend of increasing complexity in multimodal deep learning architectures. It argues that simpler, well-tuned models can often outperform more complex ones, especially when evaluated rigorously across diverse datasets and tasks. The authors emphasize the importance of methodological rigor and provide a practical checklist for future research.
Key Takeaways
- •Complex multimodal architectures don't necessarily lead to better performance.
- •Methodological rigor and hyperparameter tuning are crucial for fair comparisons.
- •A simple late-fusion Transformer (SimBaMM) can be a strong baseline.
- •The paper advocates for a shift towards methodological rigor over architectural novelty.
Reference
“The Simple Baseline for Multimodal Learning (SimBaMM) often performs comparably to, and sometimes outperforms, more complex architectures.”