@inproceedings{wang2022distilled,
author = {Wang, Zekun and Wang, Wenhui and Zhu, Haichao and Liu, Ming and Qin, Bing and Wei, Furu},
title = {Distilled Dual-Encoder Model for Vision-Language Understanding},
booktitle = {EMNLP 2022},
year = {2022},
month = {November},
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 this https URL.},
url = {http://approjects.co.za/?big=en-us/research/publication/distilled-dual-encoder-model-for-vision-language-understanding/},
}