We present a method that learns to answer visual questions by selecting image regions relevant to the text-based query. Our method maps textual queries and visual features from various regions into a shared space where they are compared for relevance with an inner product. Our method exhibits significant improvements in answering questions such as "what color," where it is necessary to evaluate a specific location, and "what room," where it selectively identifies informative image regions. Our model is tested on the recently released VQA [1] dataset, which features free-form human-annotated questions and answers.
Shih et al. introduce visual attention mechanism to highlight the image regions relevant to answering the question REF .
Where to Look: Focus Regions for Visual Question Answering
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science