Align before Fuse: Vision and Language Representation Learning with Momentum Distillation

Large-scale vision and language representation learning has shown promising improvements on various vision-language tasks. On VQA and NLVR$^2$, ALBEF achieves absolute improvements of 2.37% and 3.84% compared to the advanced, while enjoying faster inference speed.

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• 1 Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens.
• 2 Because the visual tokens and word tokens are unaligned, it is challenging for the multimodal encoder to learn image-text interactions.
• 3 In this paper, we introduce a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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