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

• URL: http://arxiv.org/abs/2203.09052v1
• Date: Thu, 17 Mar 2022 03:18:22 GMT
• Title: DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training
• Authors: Luyang Huang, Guocheng Niu, Jiachen Liu, Xinyan Xiao, Hua Wu
• Abstract summary: 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. Results show that DU-VLG yields better performance than variants trained with uni-directional generation objectives or the variant without the commitment loss.
• Score: 37.15272352614968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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