FLIPDIAL: A Generative Model for Two-Way Visual Dialogue

Daniela Massiceti, N. Siddharth, Puneet K. Dokania, Philip H. S. Torr

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present FLIPDIAL, a generative model for Visual Dialogue that simultaneously plays the role of both participants in a visually-grounded dialogue. Given context in the form of an image and an associated caption summarising the contents of the image, FLIPDIAL learns both to answer questions and put forward questions, capable of generating entire sequences of dialogue (question-answer pairs) which are diverse and relevant to the image. To do this, FLIPDIAL relies on a simple but surprisingly powerful idea: it uses convolutional neural networks (CNNs) to encode entire dialogues directly, implicitly capturing dialogue context, and conditional VAEs to learn the generative model, FLIPDIAL outperforms the state-of-the-art model in the sequential answering task (1VD) on the VisDial dataset by 5 points in Mean Rank using the generated answers. We are the first to extend this paradigm to full two-way visual dialogue (2VD), where our model is capable of generating both questions and answers in sequence based on a visual input, for which we propose a set of novel evaluation measures and metrics.
Original languageEnglish
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)978-1-5386-6420-9
ISBN (Print)978-1-5386-6421-6
Publication statusPublished - 17 Dec 2018
Event2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018

Publication series

ISSN (Print)1063-6919
ISSN (Electronic)2575-7075


Conference2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2018
Country/TerritoryUnited States
CitySalt Lake City
Internet address


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