# Image-Question-Answer Synergistic Network for Visual Dialog

arXiv: https://arxiv.org/abs/1902.09774 · HTML: https://mlsanity.com/paper/1902.09774/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 23 of 23)

- [Noisy Correspondence Learning with Meta Similarity Correction](https://mlsanity.com/paper/2304.06275/) (2023)
- [UTC: A Unified Transformer with Inter-Task Contrastive Learning for Visual Dialog](https://mlsanity.com/paper/2205.00423/) (2022)
- [GoG: Relation-aware Graph-over-Graph Network for Visual Dialog](https://mlsanity.com/paper/2109.08475/) (2021)
- [Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation](https://mlsanity.com/paper/2109.08478/) (2021)
- [Learning to Ground Visual Objects for Visual Dialog](https://mlsanity.com/paper/2109.06013/) (2021)
- [The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training](https://mlsanity.com/paper/2205.12502/) (2022)
- [MHMS: Multimodal Hierarchical Multimedia Summarization](https://mlsanity.com/paper/2204.03734/) (2022)
- [Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment](https://mlsanity.com/paper/2210.04722/) (2022)
- [InfoVisDial: An Informative Visual Dialogue Dataset by Bridging Large Multimodal and Language Models](https://mlsanity.com/paper/2312.13503/) (2023)
- [Learning Compositional Representation for Few-shot Visual Question Answering](https://mlsanity.com/paper/2102.10575/) (2021)
- [Modeling Coreference Relations in Visual Dialog](https://mlsanity.com/paper/2203.02986/) (2022)
- [Emotion and Intention Guided Multi-Modal Learning for Sticker Response Selection](https://mlsanity.com/paper/2511.17587/) (2025)
- [Reliable Cross-modal Alignment via Prototype Iterative Construction](https://mlsanity.com/paper/2510.11175/) (2025)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [SEPS: Semantic-enhanced Patch Slimming Framework for fine-grained cross-modal alignment](https://mlsanity.com/paper/2511.01390/) (2025)
- [Aligning the True Semantics: Constrained Decoupling and Distribution Sampling for Cross-Modal Alignment](https://mlsanity.com/paper/2603.05566/) (2026)

## Abstract (arXiv)

The image, question (combined with the history for de-referencing), and the corresponding answer are three vital components of visual dialog. Classical visual dialog systems integrate the image, question, and history to search for or generate the best matched answer, and so, this approach significantly ignores the role of the answer. In this paper, we devise a novel image-question-answer synergistic network to value the role of the answer for precise visual dialog. We extend the traditional one-stage solution to a two-stage solution. In the first stage, candidate answers are coarsely scored according to their relevance to the image and question pair. Afterward, in the second stage, answers with high probability of being correct are re-ranked by synergizing with image and question. On the Visual Dialog v1.0 dataset, the proposed synergistic network boosts the discriminative visual dialog model to achieve a new state-of-the-art of 57.88% normalized discounted cumulative gain. A generative visual dialog model equipped with the proposed technique also shows promising improvements.