Multimodal grid features and cell pointers for Scene Text Visual Question Answering
This work addresses the problem of answering questions based on scene text in images, which is incremental as it builds on existing methods with a novel attention-based approach.
The paper tackles scene text visual question answering by proposing a model that uses an attention mechanism to jointly reason over multimodal features, achieving competitive performance on two standard datasets.
This paper presents a new model for the task of scene text visual question answering, in which questions about a given image can only be answered by reading and understanding scene text that is present in it. The proposed model is based on an attention mechanism that attends to multi-modal features conditioned to the question, allowing it to reason jointly about the textual and visual modalities in the scene. The output weights of this attention module over the grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text the to the given question. Our experiments demonstrate competitive performance in two standard datasets. Furthermore, this paper provides a novel analysis of the ST-VQA dataset based on a human performance study.