* In terms of novelty, note that both the motivation for the model as well as the initial parts of it hold similarities to some prior works. See detailed description in the relation to prior work section. * I would be happy to see results about generalization not only for the CLEVR dataset, but also for natural images datasets where there is larger variance both in the language and visual complexity. There are multiple datasets for generalization in VQA that can be used for that such as CP-VQA and also some splits of GQA. For the CLEVR dataset, the model is basically based on using an object detector to recognize the objects and their properties and build a semantic graph that represents the image. While other approaches that are compared to for this task use object detectors as well, there are many approaches for CLEVR (such as the Neural Module Network, Relation Network, MAC and FiLM) that do not use such strong supervision and therefore the comparison between these approaches in the experimental section is not completely valid. For better comparability, I would be interested to see generalization results when these models are also being fed with at least object-based bounding-boxes representations instead of the earlier commonly used spatial features, as is very common in VQA in the last years (see bottom-up attention networks). * This can be open for debate but I personally believe that the need for reinforcement learning for a static VQA task may be a potential weakness making the approach less data efficient and harder to train the models that use gradient descent.
* This can be open for debate but I personally believe that the need for reinforcement learning for a static VQA task may be a potential weakness making the approach less data efficient and harder to train the models that use gradient descent.