Distilled Dual-encoder Model For Vision-language Understanding

Wang Zekun, Wang Wenhui, Zhu Haichao, Liu Ming, Qin Bing, Wei Furu. Arxiv 2021

[Paper] [Code]    
Applications Attention Mechanism Distillation Efficiency And Optimization Fine Tuning Has Code Merging Model Architecture Multimodal Models Pretraining Methods Tools Training Techniques

We propose a cross-modal attention distillation framework to train a dual-encoder model for vision-language understanding tasks, such as visual reasoning and visual question answering. Dual-encoder models have a faster inference speed than fusion-encoder models and enable the pre-computation of images and text during inference. However, the shallow interaction module used in dual-encoder models is insufficient to handle complex vision-language understanding tasks. In order to learn deep interactions of images and text, we introduce cross-modal attention distillation, which uses the image-to-text and text-to-image attention distributions of a fusion-encoder model to guide the training of our dual-encoder model. In addition, we show that applying the cross-modal attention distillation for both pre-training and fine-tuning stages achieves further improvements. Experimental results demonstrate that the distilled dual-encoder model achieves competitive performance for visual reasoning, visual entailment and visual question answering tasks while enjoying a much faster inference speed than fusion-encoder models. Our code and models will be publicly available at https://github.com/kugwzk/Distilled-DualEncoder.

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