# Cross-Modal Generative Augmentation for Visual Question Answering Source: [Cross-Modal Generative Augmentation for Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:50.465Z Authors: Zixu Wang, Yishu Miao, Lucia Specia Published: 2021-05-11T04:51:26.000Z ## Abstract Data augmentation has been shown to effectively improve the performance of multimodal machine learning models. This paper introduces a generative model for data augmentation by leveraging the correlations among multiple modalities. Different from conventional data augmentation approaches that apply low-level operations with deterministic heuristics, our method learns a generator that generates samples of the target modality conditioned on observed modalities in the variational auto-encoder framework. Additionally, the proposed model is able to quantify the confidence of augmented data by its generative probability, and can be jointly optimised with a downstream task. Experiments on Visual Question Answering as downstream task demonstrate the effectiveness of the proposed generative model, which is able to improve strong UpDn-based models to achieve state-of-the-art performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()