Transformer Module Networks for Systematic Generalization in Visual Question Answering

01/27/2022
by   Moyuru Yamada, et al.
0

Transformer-based models achieve great performance on Visual Question Answering (VQA). However, 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

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset