Coarse-to-Fine Reasoning for Visual Question Answering
This work addresses the VQA task, which is important for AI systems that need to understand and answer questions about visual content, and it is incremental as it builds on existing attention and relation-based methods by incorporating multi-level features.
The paper tackles the problem of bridging the semantic gap between images and questions in Visual Question Answering (VQA) by proposing a coarse-to-fine reasoning framework that utilizes features at different semantic levels, achieving superior accuracy on three large-scale VQA datasets compared to state-of-the-art methods.
Bridging the semantic gap between image and question is an important step to improve the accuracy of the Visual Question Answering (VQA) task. However, most of the existing VQA methods focus on attention mechanisms or visual relations for reasoning the answer, while the features at different semantic levels are not fully utilized. In this paper, we present a new reasoning framework to fill the gap between visual features and semantic clues in the VQA task. Our method first extracts the features and predicates from the image and question. We then propose a new reasoning framework to effectively jointly learn these features and predicates in a coarse-to-fine manner. The intensively experimental results on three large-scale VQA datasets show that our proposed approach achieves superior accuracy comparing with other state-of-the-art methods. Furthermore, our reasoning framework also provides an explainable way to understand the decision of the deep neural network when predicting the answer.