Multimodal differential network for visual question generation
Published in Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018
Recommended citation: Badri N. Patro, Sandeep Kumar, Vinod K. Kurmi, Vinay P. Namboodiri,”Multimodal Differential Network for Visual Question Generation”, 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018 https://badripatro.github.io/MDN-VQG/
Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr)
Recommended citation: Badri N. Patro, Sandeep Kumar, Vinod K. Kurmi, Vinay P. Namboodiri,”Multimodal Differential Network for Visual Question Generation”, 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018