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arXiv · 2203.09052

DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training

Luyang Huang·Guocheng Niu·Jiachen Liu·Xinyan Xiao·Hua Wu
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Abstract

Due to the limitations of the model structure and pre-training objectives, existing vision-and-language generation models cannot utilize pair-wise images and text through bi-directional generation. In this paper, we propose DU-VLG, a framework which unifies vision-and-language generation as sequence generation problems. DU-VLG is trained with novel dual pre-training tasks: multi-modal denoising autoencoder tasks and modality translation tasks. To bridge the gap between image understanding and generation, we further design a novel commitment loss. We compare pre-training objectives on image captioning and text-to-image generation datasets. Results show that DU-VLG yields better performance than variants trained with uni-directional generation objectives or the variant without the commitment loss. We also obtain higher scores compared to previous state-of-the-art systems on three vision-and-language generation tasks. In addition, human judges further confirm that our model generates real and relevant images as well as faithful and informative captions.

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Luyang Huang, Guocheng Niu, Jiachen Liu, Xinyan Xiao, Hua Wu. 2022-03-17. DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training. https://arxiv.org/abs/2203.09052

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