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The key challenge of generative Visual Dialogue (VD) systems is to respond to human queries with informative answers in natural and contiguous conversation flow.
Weighted likelihood estimation of ability in item response theory
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Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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A network-based end-to-end trainable task-oriented dialogue system
Tsung-Hsien Wen, David Vandyke, Nikola Mrksic, Milica Gasic, Lina M Rojas-Barahona, Pei-Hao Su, Stefan Ultes, and Steve Young · 2016
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Huijuan Xu and Kate Saenko · 2016
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Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo · 2016
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Visual dialog
Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, José MF Moura, Devi Parikh, and Dhruv Batra · 2017
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Best of both worlds: Transferring knowledge from discriminative learning to a generative visual dialog model
Jiasen Lu, Anitha Kannan, Jianwei Yang, Devi Parikh, and Dhruv Batra · 2017
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Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2018
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Learning answer embeddings for visual question answering
Hexiang Hu, Wei-Lun Chao, and Fei Sha · 2018
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Visual coreference resolution in visual dialog using neural module networks
Satwik Kottur, José MF Moura, Devi Parikh, Dhruv Batra, and Marcus Rohrbach · 2018
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Flipdial: A generative model for two-way visual dialogue
Daniela Massiceti, N Siddharth, Puneet K Dokania, and Philip HS Torr · 2018
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
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Multi-step reasoning via recurrent dual attention for visual dialog
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