Improving Data Augmentation for Robust Visual Question Answering with
  Effective Curriculum Learning
Yuhang Zheng Zhen Wang Long Chen
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.