What color is the chair? Like a light burgundy Brown White has white checkered A light brown Gray Brown with white texture Gray A man sitting in a chair holding a baby who is chewing on a remote Figure 1: Diverse answers generated by FLIPDIAL in the one-way visual dialogue (1VD) task. For a given time step (row), each column shows a generated answer to the current question. Answers are obtained by decoding a latent z i sampled from the conditional prior -with conditions being the image, caption and dialogue history up until that time step. 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.
REF propose FLIPDIAL model, a generative convolutional model for visual dialogue which is able to generate answers as well as generate questions based on a visual context.
FLIPDIAL: A Generative Model for Two-Way Visual Dialogue
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science