Fusion of Detected Objects in Text for Visual Question Answering

Chris Alberti, Jeffrey Ling, Michael Collins, David Reitter

Abstract
To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The “Bounding Boxes in Text Transformer” (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is highly effective on the Visual Commonsense Reasoning benchmark, achieving a new state-of-the-art with a 25% relative reduction in error rate compared to published baselines and obtaining the best performance to date on the public leaderboard (as of May 22, 2019). A detailed ablation analysis shows that the early integration of the visual features into the text analysis is key to the effectiveness of the new architecture. A reference implementation of our models is provided.
Anthology ID:
D19-1219
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2131–2140
Language:
URL:
https://preview.aclanthology.org/remove-affiliations/D19-1219/
DOI:
10.18653/v1/D19-1219
Bibkey:
Cite (ACL):
Chris Alberti, Jeffrey Ling, Michael Collins, and David Reitter. 2019. Fusion of Detected Objects in Text for Visual Question Answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2131–2140, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Fusion of Detected Objects in Text for Visual Question Answering (Alberti et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/remove-affiliations/D19-1219.pdf
Code
 google-research/language
Data
Conceptual Captions, MS COCO, VCR, Visual Question Answering
PDF Search Code Fix data