Probabilistic Neural-Symbolic Models for Interpretable Visual Question Answering
We propose a new class of probabilistic neural-symbolic models for visual question answering (VQA) that provide interpretable explanations of their decision making in the form of programs, given a small annotated set of human programs. The key idea of our approach is to learn a rich latent space which effectively propagates program annotations from known questions to novel questions. We do this by formalizing prior work on VQA, called module networks (Andreas, 2016) as discrete, structured, latent variable models on the joint distribution over questions and answers given images, and devise a procedure to train the model effectively. Our results on a dataset of compositional questions about SHAPES (Andreas, 2016) show that our model generates more interpretable programs and obtains better accuracy on VQA in the low-data regime than prior work.
 This paper proposes a discrete, structured latent variable model for visual question answering that involves compositional generalization and reasoning. In comparison to the existing approach, this paper well addressed the challenge of learning discrete latent variables in the presence of uncertainty. The results show a significant gain in performance as well as the capability of the model to generalize composition program to unseen data effectively. The qualitative analysis shows that the proposed model not only get the correct answer but also the correct behavior that leads to the answer.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Probabilistic Neural-Symbolic Models for Interpretable Visual Question Answering
We propose a new class of probabilistic neural-symbolic models for visual question answering (VQA) that provide interpretable explanations of their decision making in the form of programs, given a small annotated set of human programs. The key idea of our approach is to learn a rich latent space which effectively propagates program annotations from known questions to novel questions. We do this by formalizing prior work on VQA, called module networks (Andreas, 2016) as discrete, structured, latent variable models on the joint distribution over questions and answers given images, and devise a procedure to train the model effectively. Our results on a dataset of compositional questions about SHAPES (Andreas, 2016) show that our model generates more interpretable programs and obtains better accuracy on VQA in the low-data regime than prior work.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper proposes a discrete, structured latent variable model for visual question answering that involves compositional generalization and reasoning. In comparison to the existing approach, this paper well addressed the challenge of learning discrete latent variables in the presence of uncertainty. The results show a significant gain in performance as well as the capability of the model to generalize composition program to unseen data effectively. The qualitative analysis shows that the proposed model not only get the correct answer but also the correct behavior that leads to the answer.
assistant
 This paper proposes a discrete, structured latent variable model for visual question answering that involves compositional generalization and reasoning. In comparison to the existing approach, this paper well addressed the challenge of learning discrete latent variables in the presence of uncertainty. The results show a significant gain in performance as well as the capability of the model to generalize composition program to unseen data effectively. The qualitative analysis shows that the proposed model not only get the correct answer but also the correct behavior that leads to the answer.