Reviews: RUBi: Reducing Unimodal Biases for Visual Question Answering

Originality: The proposed method is a novel dynamic loss re-weighting technique applied to VQA under changing priors condition, aka VQA-CP, where the train and test sets are deliberately constructed to have different distributions. The related works are adequately cited and discussed. While prior works have also focused on using knowledge from a question-only model to capture unnecessary biases in the dataset [25], the paper differs from [25] in some key aspects. E.g., the proposed model guides the whole model (including the visual encoding branch) to learn "harder" examples better whereas [25] focuses on only reducing bias from question encoding. Quality: The proposed method is sound and well-motivated.