Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/116151
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Type: Conference paper
Title: Goal-oriented visual question generation via intermediate rewards
Author: Zhang, J.
Wu, Q.
Shen, C.
Zhang, J.
Lu, J.
van den Hengel, A.
Citation: Computer Vision - ECCV 2018: Proceedings, Part V, 2018 / Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (ed./s), vol.Lecture Notes in Computer Science; vol. 11209, pp.189-204
Publisher: Springer
Issue Date: 2018
ISBN: 9783030012274
ISSN: 0302-9743
1611-3349
Conference Name: 15th European Conference on Computer Vision (ECCV 2018) (08 Sep 2018 - 14 Sep 2018 : Munich)
Statement of
Responsibility: 
Junjie Zhang, Qi Wu, Chunhua Shen, Jian Zhang, Jianfeng Lu and Anton van den Hengel
Abstract: Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be an inscrutable challenge. Towards this end, we propose a Deep Reinforcement Learning framework based on three new intermediate rewards, namely goal-achieved, progressive and informativeness that encourage the generation of succinct questions, which in turn uncover valuable information towards the overall goal. By directly optimizing for questions that work quickly towards fulfilling the overall goal, we avoid the tendency of existing methods to generate long series of inane queries that add little value. We evaluate our model on the GuessWhat?! dataset and show that the resulting questions can help a standard ‘Guesser’ identify a specific object in an image at a much higher success rate.
Keywords: Goal-oriented; VQG; intermediate rewards
Rights: © Springer Nature Switzerland AG 2018
RMID: 0030101077
DOI: 10.1007/978-3-030-01228-1_12
Appears in Collections:Australian Institute for Machine Learning publications
Computer Science publications

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