Give an image, the task is to generate natural Question based on the image.
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Image Dataset-:MS-COCO and Question Dataset -: VQG-COCO
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Image Dataset-:Flickr and Question Dataset -: VQG-Bing
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Image Dataset-:Bing and Question Dataset -: VQG-Flickr
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Image Dataset-:MS-COCO and Question Dataset -: VQA-v1.0
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Image Dataset-:MS-COCO and Question Dataset -: VQA-v2.0
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Image Dataset-:MS-COCO and Question Dataset -: Visual7W
- Paper Name - Author 1 et al, Conference Year. [code]
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Visual Question Generation from Radiology Images -Sarrouti et al, ALVR workshop 2020
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Deep Bayesian Network for Visual Question Generation -Patro et al, WACV 2020. [Project Page]
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Learning to Generate Diverse Questions from Keywords -Pen et al, ICASSP 2020
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C3VQG: Category Consistent Cyclic Visual Question Generation -Uppal et al, Arxiv 2020
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Information Maximizing Visual Question Generation - Krishna et al, *CVPR 2019. [code]
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Improving Neural Question Generation Using Answer Separation -Kim et al, AAAI 2019
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Visual Question Generation: The State of the Art -Patil et al, ACM Computing Survey 2019
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Joint Learning of Question Answering and Question Generation -Sun et al, Transactions on Knowledge and Data Engineering 2019
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Multimodal Differential Network for Visual Question Generation. - Patro et al, EMNLP 2018 [Project Link]
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Visual Question Generation as Dual Task of Visual Question Answering -Yikang et al, *CVPR 2018
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Automatic Generation of Grounded Visual Questions. - Zhang et al, IJCAI 2017.
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Learning to Ask: Neural Question Generation for Reading Comprehension, -Xinya et al. ACL 2017
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Creativity: Generating Diverse Questions using Variational Autoencoders. -Jain et al, CVPR 2017.
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Image-grounded conversations: Multimodal context for natural question and response generation.. -Mostafazadeh et al,IJCNLP 2017
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Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards. -Zhang et al, Arxiv 2017
- Generating natural questions about an image. -Mostafazadeh et al, ACL 2016.
- Neural Self Talk: Image Understanding via Continuous Questioning and Answering] -Yang et al, Arxiv 2015.