Deep Learning and its applications have cascaded impactful research and development
with a diverse range of modalities present in the real-world data. More recently, this has …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Vision and Language (VL) models have demonstrated remarkable zero-shot performance in
a variety of tasks. However, some aspects of complex language understanding still remain a …

Data augmentation is a necessity to enhance data efficiency in deep learning. For vision-
language pre-training, data is only augmented either for images or for text in previous works …

Recently, the adversarial attack and defense technology has made remarkable
achievements and has been widely applied in the computer vision field, promoting its rapid …

Data Augmentation (DA)—generating extra training samples beyond the original training set—
has been widely-used in today's unbiased VQA models to mitigate language biases. Current …

Existing work on VQA explores data augmentation to achieve better generalization by
perturbing the images in the dataset or modifying the existing questions and answers. While …

Generalization beyond the training distribution is a core challenge in machine learning. The
common practice of mixing and shuffling examples when training neural networks may not …

Medical visual question answering (VQA) aims to correctly answer a clinical question related
to a given medical image. Nevertheless, owing to the expensive manual annotations of …

Visual question answering (VQA) is a task that combines both the techniques of computer
vision and natural language processing. It requires models to answer a text-based question …