Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation
This work addresses generalization challenges for medical VQA applications, but it is incremental as it builds on existing regularization and perturbation techniques.
The paper tackles the problem of poor generalization in medical visual question answering due to data scarcity by introducing gradient-guided parameter perturbations during pre-training and fine-tuning, achieving competitive results on VQA-RAD and SLAKE datasets with a smaller pre-training dataset.
Leveraging pre-trained visual language models has become a widely adopted approach for improving performance in downstream visual question answering (VQA) applications. However, in the specialized field of medical VQA, the scarcity of available data poses a significant barrier to achieving reliable model generalization. Numerous methods have been proposed to enhance model generalization, addressing the issue from data-centric and model-centric perspectives. Data augmentation techniques are commonly employed to enrich the dataset, while various regularization approaches aim to prevent model overfitting, especially when training on limited data samples. In this paper, we introduce a method that incorporates gradient-guided parameter perturbations to the visual encoder of the multimodality model during both pre-training and fine-tuning phases, to improve model generalization for downstream medical VQA tasks. The small perturbation is adaptively generated by aligning with the direction of the moving average gradient in the optimization landscape, which is opposite to the directions of the optimizer's historical updates. It is subsequently injected into the model's visual encoder. The results show that, even with a significantly smaller pre-training image caption dataset, our approach achieves competitive outcomes on both VQA-RAD and SLAKE datasets.