# Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering

arXiv: https://arxiv.org/abs/1803.11186 · HTML: https://mlsanity.com/paper/1803.11186/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2018.

## Built on by (most cited first, 13 of 13)

- [Affordances from Human Videos as a Versatile Representation for Robotics](https://mlsanity.com/paper/2304.08488/) (2023)
- [EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL](https://mlsanity.com/paper/2206.09674/) (2022)
- [Vision+X: A Survey on Multimodal Learning in the Light of Data](https://mlsanity.com/paper/2210.02884/) (2022)
- [Exploring Discrete Diffusion Models for Image Captioning](https://mlsanity.com/paper/2211.11694/) (2022)
- [Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language](https://mlsanity.com/paper/2205.06111/) (2022)
- [Ensemble of MRR and NDCG models for Visual Dialog](https://mlsanity.com/paper/2104.07511/) (2021)
- [Saying the Unseen: Video Descriptions via Dialog Agents](https://mlsanity.com/paper/2106.14069/) (2021)
- [DiffCap: Exploring Continuous Diffusion on Image Captioning](https://mlsanity.com/paper/2305.12144/) (2023)
- [Progressive Tree-Structured Prototype Network for End-to-End Image Captioning](https://mlsanity.com/paper/2211.09460/) (2022)
- [Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review](https://mlsanity.com/paper/2207.00782/) (2022)
- [Ask Questions with Double Hints: Visual Question Generation with Answer-awareness and Region-reference](https://mlsanity.com/paper/2407.05100/) (2024)
- [Adversarial Robustness of Visual Dialog](https://mlsanity.com/paper/2207.02639/) (2022)
- [GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children](https://mlsanity.com/paper/2305.16809/) (2023)

## Abstract (arXiv)

Human conversation is a complex mechanism with subtle nuances. It is hence an ambitious goal to develop artificial intelligence agents that can participate fluently in a conversation. While we are still far from achieving this goal, recent progress in visual question answering, image captioning, and visual question generation shows that dialog systems may be realizable in the not too distant future. To this end, a novel dataset was introduced recently and encouraging results were demonstrated, particularly for question answering. In this paper, we demonstrate a simple symmetric discriminative baseline, that can be applied to both predicting an answer as well as predicting a question. We show that this method performs on par with the state of the art, even memory net based methods. In addition, for the first time on the visual dialog dataset, we assess the performance of a system asking questions, and demonstrate how visual dialog can be generated from discriminative question generation and question answering.