Papers with guided models
Guiding Visual Question Generation (2022.naacl-main)
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| Challenge: | Existing approaches to Visual Question Generation (VQG) are trained to mimic an arbitrary choice of concept but only one or a few are captured by the human references. |
| Approach: | They propose a variant of Visual Question Generation which conditions the question generator on categorical information based on expectations on the type of question and the objects it should explore. |
| Outcome: | The proposed model improves on the current state of the art on an answer-category augmented VQA dataset and human evaluation validates that guidance helps the generation of questions that are grammatically coherent and relevant to the given image and objects. |