@article {5015,
	title = {Transformer Module Networks for Systematic Generalization in Visual Question Answering},
	number = {121},
	year = {2022},
	month = {02/2022},
	abstract = {Transformer-based models achieve great performance on Visual Question Answering (VQA). How- ever, when we evaluate them on systematic generalization, i.e., handling novel combinations of known concepts, their performance degrades. Neural Module Networks (NMNs) are a promising approach for systematic generalization that consists on composing modules, i.e., neural networks that tackle a sub-task. Inspired by Transformers and NMNs, we propose Transformer Module Network (TMN), a novel Transformer-based model for VQA that dynamically composes modules into a question-specific Transformer network. TMNs achieve state-of-the-art systematic generalization performance in three VQA datasets, namely, CLEVR-CoGenT, CLOSURE and GQA-SGL, in some cases improving more than 30\% over standard Transformers.
},
	author = {Moyuru Yamada and Vanessa D{\textquoteright}Amario and Kentaro Takemoto and Xavier Boix and Tomotake Sasaki}
}
