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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 ensemblingdoi:10.18653/v1/n18-1201 dblp:conf/naacl/RajaniM18 fatcat:zc57ldjrjfbg3abp2pvilo26ai