Abstract
Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via composition of modules that tackle a sub-task. NMNs are a promising strategy to achieve systematic generalization, i.e. overcoming biasing factors in the training distribution. However, the aspects of NMNs that facilitate systematic generalization are not fully understood. In this paper, we demonstrate that the stage and the degree at which modularity is defined has large influence on systematic generalization. In a series of experiments on three VQA datasets (MNIST with multiple attributes, SQOOP, and CLEVR-CoGenT), our results reveal that tuning the degree of modularity in the network, especially at the image encoder stage, reaches substantially higher systematic generalization. These findings lead to new NMN architectures that outperform previous ones in terms of systematic generalization.
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URL
https://arxiv.org/abs/2106.08170
