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

Code

• github.com/salesforce/lavis
  Officialpytorch★ 11,193
• github.com/salesforce/ALBEF
  In paperpytorch★ 1,757
• github.com/facebookresearch/multimodal
  pytorch★ 1,706
• github.com/amazon-research/mix-generation
  pytorch★ 126
• github.com/salesforce/pb-ovd
  pytorch★ 64
• github.com/yuliangcai2022/clumo
  pytorch★ 8

Abstract

Large-scale vision and language representation learning has shown promising improvements on various vision-language tasks. Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens. Because the visual tokens and word tokens are unaligned, it is challenging for the multimodal encoder to learn image-text interactions. 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. Unlike most existing methods, our method does not require bounding box annotations nor high-resolution images. In order to improve learning from noisy web data, we propose momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model. We provide a theoretical analysis of ALBEF from a mutual information maximization perspective, showing that different training tasks can be interpreted as different ways to generate views for an image-text pair. ALBEF achieves state-of-the-art performance on multiple downstream vision-language tasks. On image-text retrieval, ALBEF outperforms methods that are pre-trained on orders of magnitude larger datasets. On VQA and NLVR^2, ALBEF achieves absolute improvements of 2.37% and 3.84% compared to the state-of-the-art, while enjoying faster inference speed. Code and pre-trained models are available at https://github.com/salesforce/ALBEF/.

Tasks

Benchmark Results

Reproductions