# Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning Source: [Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:16:25.405Z Authors: Yuhang Zheng, Zhen Wang, Long Chen Published: 2024-01-28T12:48:16.000Z ## Abstract Being widely used in learning unbiased visual question answering (VQA) models, Data Augmentation (DA) helps mitigate language biases by generating extra training samples beyond the original samples. While today's DA methods can generate robust samples, the augmented training set, significantly larger than the original dataset, often exhibits redundancy in terms of difficulty or content repetition, leading to inefficient model training and even compromising the model performance. To this end, we design an Effective Curriculum Learning strategy ECL to enhance DA-based VQA methods. Intuitively, ECL trains VQA models on relatively \`\`easy'' samples first, and then gradually changes to \`\`harder'' samples, and less-valuable samples are dynamically removed. Compared to training on the entire augmented dataset, our ECL strategy can further enhance VQA models' performance with fewer training samples. Extensive ablations have demonstrated the effectiveness of ECL on various methods. ## 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]()