Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a new task and solution for vision and language: generation of grounded visual questions. Visual question ansring (VQA) is an emerging topic which links textual questions with visual input. To the best of our knowledge, it lacks automatic method to generate reasonable and versatile questions. So far, almost all the textual questions are generated manually, as ll as the corresponding ansrs. To this end,  propose a system that automatically generates visually grounded questions . First, visual input is analyzed with deep caption model. Second, the captions along with VGG-16 features are used as input for our proposed question generator to generate visually grounded questions. Finally, to enable generating of versatile questions, a question type selection module is provided which selects reasonable question types and provide them as parameters for question generation. This is done using a hybrid LSTM with both visual and ansr input. Our system is trained using VQA and Visual7W dataset and shows reasonable results on automatically generating of new visual questions.  also propose a quantitative metric for automatic evaluation of the question quality.
['Shijie Zhang', 'Lizhen Qu', 'Shaodi You', 'Zhenglu Yang', 'Jiawan Zhang']
I am writing a research paper on a idea Automatic Generation of Grounded Visual Questions
[{"from": "human", "value": "I am writing a research paper on a idea Automatic Generation of Grounded Visual Questions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a new task and solution for vision and language: generation of grounded visual questions. Visual question ansring (VQA) is an emerging topic which links textual questions with visual input. To the best of our knowledge, it lacks automatic method to generate reasonable and versatile questions. So far, almost all the textual questions are generated manually, as ll as the corresponding ansrs. To this end,  propose a system that automatically generates visually grounded questions . First, visual input is analyzed with deep caption model. Second, the captions along with VGG-16 features are used as input for our proposed question generator to generate visually grounded questions. Finally, to enable generating of versatile questions, a question type selection module is provided which selects reasonable question types and provide them as parameters for question generation. This is done using a hybrid LSTM with both visual and ansr input. Our system is trained using VQA and Visual7W dataset and shows reasonable results on automatically generating of new visual questions.  also propose a quantitative metric for automatic evaluation of the question quality."}]