Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets

Wei-Lun Chao, Hexiang Hu, Fei Sha


Abstract
Visual question answering (Visual QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve. In this paper, we study a crucial component of this task: how can we design good datasets for the task? We focus on the design of multiple-choice based datasets where the learner has to select the right answer from a set of candidate ones including the target (i.e., the correct one) and the decoys (i.e., the incorrect ones). Through careful analysis of the results attained by state-of-the-art learning models and human annotators on existing datasets, we show that the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or both while still doing well on the task. Inspired by this, we propose automatic procedures to remedy such design deficiencies. We apply the procedures to re-construct decoy answers for two popular Visual QA datasets as well as to create a new Visual QA dataset from the Visual Genome project, resulting in the largest dataset for this task. Extensive empirical studies show that the design deficiencies have been alleviated in the remedied datasets and the performance on them is likely a more faithful indicator of the difference among learning models. The datasets are released and publicly available via http://www.teds.usc.edu/website_vqa/.
Anthology ID:
N18-1040
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
431–441
Language:
URL:
https://aclanthology.org/N18-1040
DOI:
10.18653/v1/N18-1040
Bibkey:
Cite (ACL):
Wei-Lun Chao, Hexiang Hu, and Fei Sha. 2018. Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 431–441, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets (Chao et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1040.pdf
Note:
 N18-1040.Notes.pdf
Video:
 http://vimeo.com/276455887
Data
COCOCOCO-QAVisual GenomeVisual Question AnsweringVisual7W