X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers

Jaemin Cho, Jiasen Lu, Dustin Schwenk, Hannaneh Hajishirzi, Aniruddha Kembhavi


Abstract
Mirroring the success of masked language models, vision-and-language counterparts like VILBERT, LXMERT and UNITER have achieved state of the art performance on a variety of multimodal discriminative tasks like visual question answering and visual grounding. Recent work has also successfully adapted such models towards the generative task of image captioning. This begs the question: Can these models go the other way and generate images from pieces of text? Our analysis of a popular representative from this model family – LXMERT – finds that it is unable to generate rich and semantically meaningful imagery with its current training setup. We introduce X-LXMERT, an extension to LXMERT with training refinements including: discretizing visual representations, using uniform masking with a large range of masking ratios and aligning the right pre-training datasets to the right objectives which enables it to paint. X-LXMERT’s image generation capabilities rival state of the art generative models while its question answering and captioning abilities remains comparable to LXMERT. Finally, we demonstrate the generality of these training refinements by adding image generation capabilities into UNITER to produce X-UNITER.
Anthology ID:
2020.emnlp-main.707
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8785–8805
Language:
URL:
https://aclanthology.org/2020.emnlp-main.707
DOI:
10.18653/v1/2020.emnlp-main.707
Bibkey:
Cite (ACL):
Jaemin Cho, Jiasen Lu, Dustin Schwenk, Hannaneh Hajishirzi, and Aniruddha Kembhavi. 2020. X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8785–8805, Online. Association for Computational Linguistics.
Cite (Informal):
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers (Cho et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.707.pdf
Video:
 https://slideslive.com/38938675
Code
 allenai/x-lxmert
Data
GQAVisual GenomeVisual Question Answering v2.0