Towards Unconstrained Pointing Problem of Visual Question Answering: A Retrieval-based Method
Wenlong Cheng, Yan Huang, Liang Wang · 2018
The pointing problem of visual question answering (VQA) is that given an image and a question which asks for the location of the interested object, find a region that answers the question. It is an important research problem in VQA tasks and has many potential applications in our daily life. Most of the existing work on this task can only solve it in the form of multiple choices, i.e., given candidate answers in advance, and then selecting a correct one. In this paper, we propose a retrieval model, which can not only deal with the multiple-choices task, but also provide a feasible solution for the no-candidate-answer task. The principle of our method is to pull the question and correct answer close, and push the question and incorrect answer away in a common feature space. To our best knowledge, we are the first to use retrieval method to solve the unconstrained (no-candidate-answer) pointing problem of VQA. Furthermore, our proposed method outperforms the state-of-the-art methods on the Visual7W [1] dataset in terms of the pointing problem of VQA.