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Stacking with Auxiliary Features for Visual Question Answering
2018
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Visual Question Answering (VQA) is a wellknown 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
doi:10.18653/v1/n18-1201
dblp:conf/naacl/RajaniM18
fatcat:zc57ldjrjfbg3abp2pvilo26ai