Question‐aware prediction with candidate answer recommendation for visual question answering
Geonu Kim, Jinhwan Kim · Electronics Letters · 2017
An approach for visual question answering is described. The proposed network solves an open‐ended problem with candidate answer recommendation, which is generated solely from the given question. Then, the score from the proposed question‐aware prediction module and the score from candidate answer recommendation module are combined to determine the final composite score. The proposed approach uses the bag‐of‐words framework to understand questions, instead of a complex and neural‐network‐based module; therefore, an additional dataset to pre‐train the language model is not required. Although the proposed approach does not achieve the state‐of‐the‐art performance overall, the approach performs the best for certain types of questions with a small amount of training data.