Feature Alignment Determines Fusion Strategy: A Comparative Study of Cross-Attention and Concatenation in Multimodal Learning
arXiv:2606.01207v1 Announce Type: cross Abstract: The choice between cross-attention and concatenation for multimodal fusion remains governed by practitioner intuition rather than principled understanding. In this paper, we demonstrate that feature alignment quality, not data scale alone, is the primary determinant of which fusion strategy excels. Through controlled experiments on Flickr8k using two feature extraction backbones (ResNet18 and CLIP ViT-B/32), we show that concatenation outperforms cross-attention by 4.1-5.1 percentage points across all tested scales (2048-16384 samples) when fea
The proliferation of multimodal AI models necessitates a deeper understanding of optimal fusion techniques to enhance performance and efficiency.
This research provides a principled understanding for designing multimodal AI systems, moving beyond trial-and-error to systemically improve model robustness and accuracy.
The debate around cross-attention versus concatenation is resolved under specific conditions, guiding practitioners to prioritize feature alignment for superior multimodal fusion.
- · AI Researchers
- · Multimodal AI Developers
- · Companies deploying multimodal AI
- · Inefficient multimodal AI architectures
Improved performance and efficiency in multimodal AI applications leveraging better fusion strategies.
Accelerated development of more robust AI agents and systems capable of processing diverse input types effectively.
Reduced computational waste and development cycles in building multimodal AI, indirectly impacting the energy footprint of AI.
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