Distilled Dual-Encoder Model for Vision-Language Understanding

• URL: http://arxiv.org/abs/2112.08723v1
• Date: Thu, 16 Dec 2021 09:21:18 GMT
• Title: Distilled Dual-Encoder Model for Vision-Language Understanding
• Authors: Zekun Wang, Wenhui Wang, Haichao Zhu, Ming Liu, Bing Qin, Furu Wei
• Abstract summary: We propose a cross-modal attention distillation framework to train a dual-encoder model for vision-language understanding tasks. We show that applying the cross-modal attention distillation for both pre-training and fine-tuning stages achieves further improvements.
• Score: 50.42062182895373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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