# Modeling Coreference Relations in Visual Dialog

arXiv: https://arxiv.org/abs/2203.02986 · HTML: https://mlsanity.com/paper/2203.02986/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 38 references that are in the map.
- First posted on arXiv 6 Mar 2022.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline](https://mlsanity.com/paper/1912.02379/) (2019)
- [Visual Coreference Resolution in Visual Dialog using Neural Module Networks](https://mlsanity.com/paper/1809.01816/) (2018)
- [Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog](https://mlsanity.com/paper/1902.00579/) (2019)
- [Factor Graph Attention](https://mlsanity.com/paper/1904.05880/) (2019)
- [Recursive Visual Attention in Visual Dialog](https://mlsanity.com/paper/1812.02664/) (2018)
- [DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue](https://mlsanity.com/paper/1911.07251/) (2019)
- [Dual Attention Networks for Visual Reference Resolution in Visual Dialog](https://mlsanity.com/paper/1902.09368/) (2019)
- [Image-Question-Answer Synergistic Network for Visual Dialog](https://mlsanity.com/paper/1902.09774/) (2019)
- [Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model](https://mlsanity.com/paper/1706.01554/) (2017)
- [Iterative Context-Aware Graph Inference for Visual Dialog](https://mlsanity.com/paper/2004.02194/) (2020)

## Built on by (most cited first, 3 of 3)

- [McQueen: a Benchmark for Multimodal Conversational Query Rewrite](https://mlsanity.com/paper/2210.12775/) (2022)
- [mathbbVD-mathbbGR: Boosting mathbbVisual mathbbDialog with Cascaded Spatial-Temporal Multi-Modal mathbbGRaphs](https://mlsanity.com/paper/2310.16590/) (2023)
- [Adversarial Robustness of Visual Dialog](https://mlsanity.com/paper/2207.02639/) (2022)

## Abstract (arXiv)

Visual dialog is a vision-language task where an agent needs to answer a series of questions grounded in an image based on the understanding of the dialog history and the image. The occurrences of coreference relations in the dialog makes it a more challenging task than visual question-answering. Most previous works have focused on learning better multi-modal representations or on exploring different ways of fusing visual and language features, while the coreferences in the dialog are mainly ignored. In this paper, based on linguistic knowledge and discourse features of human dialog we propose two soft constraints that can improve the model's ability of resolving coreferences in dialog in an unsupervised way. Experimental results on the VisDial v1.0 dataset shows that our model, which integrates two novel and linguistically inspired soft constraints in a deep transformer neural architecture, obtains new state-of-the-art performance in terms of recall at 1 and other evaluation metrics compared to current existing models and this without pretraining on other vision-language datasets. Our qualitative results also demonstrate the effectiveness of the method that we propose.