Data Augmentation for Visual Question Answering
Data augmentation is widely used to train deep neural networks for image classification tasks. Simply flipping images can help learning tremendously by increasing the number of training images by a factor of two. However, little work has been done studying data augmentation in natural language processing. Here, we describe two methods for data augmentation for Visual Question Answering (VQA). The first uses existing semantic annotations to generate new questions. The second method is a generative approach using recurrent neural networks. Experiments show that the proposed data augmentation improves performance of both baseline and state-of-the-art VQA algorithms.
PDF Abstract
Datasets
MS COCO
Visual Question Answering
VQG
COCO-QA
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
