Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering
<|reference_start|>Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering: Scene-Text Visual Question Answering (ST-VQA) aims to understand scene text in images and answer questions related to the text content. Most existing methods heavily rely on the accuracy of Optical Character Recognition (OCR) systems, and aggressive fine-tuning based on limited spatial location information and erroneous OCR text information often leads to inevitable overfitting. In this paper, we propose a multimodal adversarial training architecture with spatial awareness capabilities. Specifically, we introduce an Adversarial OCR Enhancement (AOE) module, which leverages adversarial training in the embedding space of OCR modality to enhance fault-tolerant representation of OCR texts, thereby reducing noise caused by OCR errors. Simultaneously, We add a Spatial-Aware Self-Attention (SASA) mechanism to help the model better capture the spatial relationships among OCR tokens. Various experiments demonstrate that our method achieves significant performance improvements on both the ST-VQA and TextVQA datasets and provides a novel paradigm for multimodal adversarial training.<|reference_end|>
@article{shen2024adversarial,
    title={Adversarial Training with OCR Modality Perturbation for Scene-Text
  Visual Question Answering},
    author={Zhixuan Shen, Haonan Luo, Sijia Li, Tianrui Li},
    journal={arXiv preprint arXiv:2403.09288},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09288},
    primaryClass={cs.CV cs.AI}
}