Stacking with Auxiliary Features for Visual Question Answering

Nazneen Fatema Rajani, Raymond Mooney


Abstract
Visual Question Answering (VQA) is a well-known and challenging task that requires systems to jointly reason about natural language and vision. Deep learning models in various forms have been the standard for solving VQA. However, some of these VQA models are better at certain types of image-question pairs than other models. Ensembling VQA models intelligently to leverage their diverse expertise is, therefore, advantageous. Stacking With Auxiliary Features (SWAF) is an intelligent ensembling technique which learns to combine the results of multiple models using features of the current problem as context. We propose four categories of auxiliary features for ensembling for VQA. Three out of the four categories of features can be inferred from an image-question pair and do not require querying the component models. The fourth category of auxiliary features uses model-specific explanations. In this paper, we describe how we use these various categories of auxiliary features to improve performance for VQA. Using SWAF to effectively ensemble three recent systems, we obtain a new state-of-the-art. Our work also highlights the advantages of explainable AI models.
Anthology ID:
N18-1201
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
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2217–2226
Language:
URL:
https://aclanthology.org/N18-1201
DOI:
10.18653/v1/N18-1201
Bibkey:
Cite (ACL):
Nazneen Fatema Rajani and Raymond Mooney. 2018. Stacking with Auxiliary Features for Visual Question Answering. 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 2217–2226, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Stacking with Auxiliary Features for Visual Question Answering (Rajani & Mooney, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1201.pdf
Data
Visual GenomeVisual Question Answering