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https://github.com/ImKeTT/Awesome-Multi-Modal-Dialog
[Paperlist] Awesome paper list of multimodal dialog, including methods, datasets and metrics
https://github.com/ImKeTT/Awesome-Multi-Modal-Dialog
List: Awesome-Multi-Modal-Dialog
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[Paperlist] Awesome paper list of multimodal dialog, including methods, datasets and metrics
- Host: GitHub
- URL: https://github.com/ImKeTT/Awesome-Multi-Modal-Dialog
- Owner: ImKeTT
- License: apache-2.0
- Created: 2022-09-28T13:32:30.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2022-10-10T03:36:41.000Z (almost 2 years ago)
- Last Synced: 2024-05-23T00:02:56.662Z (4 months ago)
- Topics: awesome-list, dialogue, dialogue-system, multimodal, multimodal-datasets, multimodal-deep-learning, multimodal-dialogue, multimodal-learning, paperlist
- Homepage:
- Size: 162 KB
- Stars: 34
- Watchers: 2
- Forks: 4
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Awesome Paper List of Multi-Modal Dialogue
Papers, codes and resources about multi-modal dialogue, including methods, datasets and related metrics.
We split the multi-modal dialogue task to **Visual-Grounded Dialogue (VGD, including Visual QA or VQA), Visual Question Generation, Multimodal Conversation (MMC)** and **Visual Navigation (VN)**.
## Datasets
| Dataset | Task | Publisher | Author |
| :----------------------------------------------------------: | :--------------------------------: | :---------: | :-----------------: |
| [VQA: Visual Question Answering](http://cloudcv.org/vqa) | visual QA (VQA) | ICCV 2015 | Virginia Tech |
| [Visual Dialog](https://visualdialog.org/data) | visual QA (VQA) | CVPR 2017 | VisualDialog Org. |
| [GuessWhat?! Visual object discovery through multi-modal dialogue](https://guesswhat.ai/download) | visual QA (VQA) | CVPR 2017 | Montreal Univ. |
| [Visual Reference Resolution using Attention Memory for Visual Dialog](http://cvlab.postech.ac.kr/research/attmem) | visual QA (VQA) | NIPS 2017 | Postech&Disney |
| [CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning](https://cs.stanford.edu/people/jcjohns/clevr/) | visual QA (VQA) | CVPR 2017 | Stanford |
| [Image-grounded conversations: Multimodal context for natural question and response generation (IGC)](https://www.microsoft.com/en-us/download/details.aspx?id=55324) | visual QA (VQA) | IJCNLP 2017 | Rochester&Microsoft |
| [Towards Building Large Scale Multimodal Domain-Aware Conversation Systems (MMD)](https://amritasaha1812.github.io/MMD/) | multimodal conv. (MMC) | AAAI 2018 | IBM |
| [Embodied Question Answering (EQA)](https://embodiedqa.org/) | visual QA (VQA) | CVPR 2018 | Facebook |
| [Talk the walk: Navigating new york city through grounded dialogue](https://github.com/facebookresearch/talkthewalk) | visual navigation (VN) | ICLR 2019 | MILA |
| [Vision-and-Dialog Navigation](https://cvdn.dev/) | visual navigation (VN) | CoRL 2019 | UoW |
| [CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog](https://github.com/satwikkottur/clevr-dialog) | visual-grounded dialog (VGD) | NAACL 2019 | CMU |
| [Image-Chat: Engaging Grounded Conversations](http://parl.ai/projects/image_chat) | visual-grounded dialog (VGD) | ACL2020 | Facebook |
| [OpenViDial](https://github.com/ShannonAI/OpenViDial) | visual-grounded dialog (VGD) | arxiv 2020 | ShannonAI |
| [Situated and Interactive Multimodal Conversations (SIMMC)](https://github.com/facebookresearch/simmc) | multimodal conv./visual navigation | COLING 2020 | Facebook |
| [PhotoChat: A human-human dialogue dataset with photo sharing behavior for joint image-text modeling](https://github.com/google-research/google-research/tree/master/multimodalchat/) | multimodal conv. (MMC) | ACL 2021 | Google |
| [MMConv: An Environment for Multimodal Conversational Search across Multiple Domains](https://github.com/lizi-git/MMConv) | multimodal conv. (MMC) | SIGIR 2021 | NUS |
| [Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images](https://github.com/shh1574/multi-modal-dialogue-dataset) | multimodal conv. (MMC) | ACL2021 | KAIST |
| [OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts](https://github.com/ShannonAI/OpenViDial) | visual-grounded dialog (VGD) | arxiv 2021 | ShannonAI |
| [MMChat: Multi-Modal Chat Dataset on Social Media](https://github.com/silverriver/MMChat) | visual-grounded dialog (VGD) | LREC 2022 | Alibaba |
| [MSCTD: A Multimodal Sentiment Chat Translation Dataset](https://github.com/XL2248/MSCTD) | visual-grounded dialog (VGD) | arxiv 2022 | Tencent |
## Methods
We roughly split the learning paradigm of different methods (if available) as: *Fusion-Based (FB)* and *Attention-Based (AB)*.
- *Fusion-based (FB)*: Simple concatenation of multi-modal information at the model input.
- *Attention-Based (AB)*: Co-attention between different modalities to learn their relations.
### Visual Grounded Dialogue
Visual grounded dialogue considers only **one image** for one dialogue session. The whole session is constrained to this given image. It is also know as *Visual Dialog* task.
| Title | Dataset Used | Publisher | Code | Class |
| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------: | :----------------------------------------------------------: | :---: |
| [Visual Dialog](https://arxiv.org/abs/1611.08669) | VisDial v0.9 | ICCV 2017 | CODE | FB |
| [Open Domain Dialogue Generation with Latent Images](https://arxiv.org/abs/2004.01981) | Image-Chat; Reddit | AAAI 2021 | CODE | FB |
| [Maria: A Visual Experience Powered Conversational Agent](https://arxiv.org/abs/2105.13073) | Reddit; Conceptual Caption | ACL 2021 | [CODE](https://github.com/jokieleung/Maria) | FB |
| [Learning to Ground Visual Objects for Visual Dialog](https://arxiv.org/abs/2109.06013) | VisDial v0.9, v1.0; MS-COCO | ACL 2021 | CODE | FB |
| [Multi-Modal Open-Domain Dialogue](https://arxiv.org/abs/2010.01082) | Image-Chat; ConvAI2; EmpatheticDialogues; Wizard of WikiPedia; BlendedSkillTalk | EMNLP 2021 | CODE | FB |
| [Iterative Context-Aware Graph Inference for Visual Dialog](https://arxiv.org/abs/2004.02194) | VisDial v0.9, v1.0 | CVPR 2020 | [CODE](https://github.com/wh0330/CAG_VisDial) | FB |
| [Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer](https://arxiv.org/abs/2004.06698) | VisDial v1.0 | EMNLP 2021 | [CODE](https://github.com/gicheonkang/SGLKT-VisDial) | FB |
| [VD-BERT: A Unified Vision and Dialog Transformer with BERT](https://arxiv.org/abs/2004.13278) | VisDial v0.9, v1.0 | EMNLP 2020 | [CODE](https://github.com/salesforce/VD-BERT) | FB |
| [GuessWhat?! Visual object discovery through multi-modal dialogue](https://arxiv.org/abs/1611.08481) | Guessing;MNIST Dialog | CVPR 2017 | [CODE](https://github.com/GuessWhatGame/guesswhat) | FB |
| [Ask No More: Deciding when to guess in referential visual dialogue](https://arxiv.org/abs/1805.06960) | Guessing | COLING 2018 | [CODE](https://vista-unitn-uva.github.io) | FB |
| [Visual Reference Resolution using Attention Memory for Visual Dialog](https://papers.nips.cc/paper/2017/hash/654ad60ebd1ae29cedc37da04b6b0672-Abstract.html) | MNIST Dialog; VisDial v1.0 | NIPS 2017 | CODE | AB |
| [Visual Coreference Resolution in Visual Dialog using Neural Module Networks](https://arxiv.org/abs/1809.01816) | MNIST Dialog; VisDial | ECCV 2018 | [CODE](https://github.com/facebookresearch/corefnmn) | AB |
| [Dual Attention Networks for Visual Reference Resolution in Visual Dialog](https://arxiv.org/abs/1902.09368) | VisDial v1.0, v0.9 | EMNLP 2019 | [CODE](https://github.com/gicheonkang/DAN-VisDial) | AB |
| [Efficient Attention Mechanism for Visual Dialog that can Handle All the Interactions between Multiple Inputs (LTMI)](https://arxiv.org/abs/1911.11390) | VisDial v1.0 | ECCV 2020 | [CODE](https://github.com/davidnvq/visdial) | AB |
| [Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline](https://arxiv.org/abs/1912.02379) | Wikipedia; BooksCorpus; Conceptual Cations; VQA; VisDial v1.0 | ECCV 2019 | [CODE](https://github.com/vmurahari3/visdial-bert) | AB |
| [Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog](https://arxiv.org/abs/1902.00579) | VisDial v1.0; MS-COCO | ACL 2019 | CODE | AB |
| [Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning](https://arxiv.org/abs/1711.07613) | VisDial v0.9 | CVPR 2018 | CODE | AB |
| [Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model](https://arxiv.org/abs/1706.01554) | VisDial v0.9 | NeurIPS 2017 | [CODE](https://github.com/jiasenlu/visDial.pytorch) | AB |
| [Multi-View Attention Network for Visual Dialog](https://arxiv.org/abs/2004.14025) | VisDial v1.0, v0.9 | arxiv 2020 | [CODE](https://github.com/batra-mlp-lab/visdial-challenge-starter-pytorch) | AB |
| [Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning](https://arxiv.org/abs/1703.06585) | VisDial | ICCV 2017 | [CODE](https://github.com/batra-mlp-lab/visdial-rl) | FB |
| [Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog](https://arxiv.org/abs/1805.03257) | VisDial | SIGDIAL 2018 | CODE | FB |
| [Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat](https://arxiv.org/abs/1809.03408) | GuessingWhat?! | NAACL 2019 | [CODE](https://vista-unitn-uva.github.io) | FB |
### Multi-modal Conversation
Multi-modal conversation (MMC) aims at conducting conversations with multiple images. Models should understand multiple images and/or generate multi-modal responses during conversation.
| Title | Dataset Used | Publisher | Code | Class |
| :----------------------------------------------------------: | :----------------------------------------------------------: | :--------: | :----------------------------------------------------------: | :---: |
| [Multimodal Dialogue Response Generation](https://arxiv.org/abs/2110.08515) | PhotoChat; Reddit; YFCC100M | ACL 2022 | CODE | FB |
| [Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images](https://arxiv.org/abs/2107.08685) | DailyDialog; Empathetic Dialog; Persona Chat; MS-COCO; Flicker30K | ACL 2021 | [CODE](https://github.com/shh1574/multi-modal-dialogue-dataset) | FB |
| [Towards Enriching Responses with Crowd-sourced Knowledge for Task-oriented Dialogue](https://dl.acm.org/doi/10.1145/3475959.3485392) | MMConv. | MuCAI 2021 | CODE | FB |
| [Multimodal Dialog System: Generating Responses via Adaptive Decoders](https://liqiangnie.github.io/paper/fp349-nieAemb.pdf) | MMD | MM 2019 | [CODE](https://acmmultimedia.wixsite.com/magic) | FB |
| [Multimodal Dialog Systems via Capturing Context-aware Dependencies of Semantic Elements](http://staff.ustc.edu.cn/~tongxu/Papers/Weidong_MM20.pdf) | MMD | MM 2020 | [CODE](ttps://github.com/githwd2016/MATE) | FB |
| [Multimodal Dialog System: Relational Graph-based Context-aware Question Understanding](https://dl.acm.org/doi/abs/10.1145/3474085.3475234) | MMD | MM 2021 | CODE | FB |
| [User Attention-guided Multimodal Dialog Systems](https://dl.acm.org/doi/10.1145/3331184.3331226) | MMD | SIGIR 2019 | [CODE]( https://github.com/ChenTsuei/UMD) | FB |
| [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://arxiv.org/abs/2109.05778) | DailyDialog; Flickr30K; PersonaChat | MM 2021 | CODE | AB |
### Question Generation
Question generation task generates questions instead of responses based on given images. It is similar to visual-grounded dialogue.
| Title | Dataset Used | Publisher | Code |
| :----------------------------------------------------------: | :------------------: | :-------: | :--------------------------------------------------: |
| [Category-Based Strategy-Driven Question Generator for Visual Dialogue](https://aclanthology.org/2021.ccl-1.89.pdf) | GuessingWhat?! | CCL 2021 | CODE |
| [Visual Dialogue State Tracking for Question Generation](https://arxiv.org/abs/1911.07928) | GuessingWhat?! | AAAI 2020 | CODE |
| [Answer-Driven Visual State Estimator for Goal-Oriented Visual Dialogue](https://dl.acm.org/doi/10.1145/3394171.3413668) | GuessWhat?!; MS-COCO | MM 2020 | [CODE](https://github.com/zipengxuc/ADVSE-GuessWhat) |
| [Goal-Oriented Visual Question Generation via Intermediate Re- wards](https://openaccess.thecvf.com/content_ECCV_2018/papers/Junjie_Zhang_Goal-Oriented_Visual_Question_ECCV_2018_paper.pdf) | GuessWhat?! | ECCV 2018 | CODE |
| [Learning goal-oriented visual dialog via tempered policy gradient](https://arxiv.org/abs/1807.00737) | GuessWhat?! | SLT 2018 | CODE |
| [Information maximizing visual question generation](https://arxiv.org/abs/1903.11207) | VQG | ICCV 2019 | CODE |
### Visual Navigation
Visual navigation focuses on guiding users to their destination from their starter points given surrounding information. Here we mainly collect methods that involve conversational guidance in language.
| Title | Dataset Used | Publisher | Code |
| :----------------------------------------------------------: | :----------------------------------------------------------: | :--------: | :----------------------------------------------------------: |
| [Learning to interpret natural language navigation instructions from observations](http://www.cs.utexas.edu/~ai-lab/pubs/chen.aaai11.pdf) | [Walk the Talk](https://www.aaai.org/Papers/AAAI/2006/AAAI06-232.pdf) | AAAI 2011 | CODE |
| [Talk the walk: Navigating new york city through grounded dialogue](https://arxiv.org/abs/1807.03367) | Talk the Walk; GuessWhat?! | Arxiv 2018 | [CODE](https://github.com/facebookresearch/talkthewalk) |
| [Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction](https://arxiv.org/abs/1811.04179) | [LANI](https://arxiv.org/abs/1809.00786) | CoRL 2018 | [CODE](https://github.com/lil-lab/drif) |
| [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://arxiv.org/abs/1711.07280) | [R2R](https://github.com/google-research/google-research/tree/master/r4r); VQA; [Matterport3D](https://arxiv.org/abs/1709.06158) | CVPR 2018 | [CODE](https://github.com/peteanderson80/Matterport3DSimulator) |
| [Embodied Question Answering](https://arxiv.org/abs/1711.11543) | EQA; VQA | CVPR 2018 | [CODE](https://github.com/facebookresearch/EmbodiedQA) |
| [IQA: Visual Question Answering in Interactive Environments](https://arxiv.org/abs/1712.03316) | IQUAD; VQA; AI2-THOR | CVPR 2018 | [CODE](https://github.com/danielgordon10/thor-iqa-cvpr-2018) |
| [Natural language navigation and spatial reasoning in visual street environments](https://arxiv.org/abs/1811.12354) | Touchdown; VQA; ReferItGame; Google Refexp; Talk the Walk | CVPR 2019 | [CODE](https://github.com/lil-lab/touchdown) |
| [Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation](https://arxiv.org/abs/1905.12255) | [R2R; R4R](https://github.com/google-research/google-research/tree/master/r4r) | ACL 2019 | CODE |
| [Learning to navigate unseen environments: Back translation with environmental dropout](http://aclanthology.lst.uni-saarland.de/N19-1268.pdf) | Matterport3D | NAACL 2019 | [CODE](https://github.com/airsplay/R2R-EnvDrop) |
| [Touchdown: Natural language navigation and spatial reasoning in visual street environments](https://arxiv.org/abs/1811.12354) | Touchdown; VQA; ReferItGame; Google Refexp; Talk the Walk | CVPR 2019 | [CODE](https://github.com/lil-lab/touchdown) |
## Metrics
A summary paper of visual dialogue metrics: [A Revised Generative Evaluation of Visual Dialogue](https://arxiv.org/abs/2004.09272), [CODE](https://github.com/danielamassiceti/geneval_visdial).
We split the related metrics to *Rank-based* and *Generiate-based*
- *Rank-based*: measures the quality of responses that **retrieve** from response candidates.
- *Generate-based*: measures the quality of responses that **generate** from the model.
#### Rank-based Metrics
| Metrics | Better indicator | Explanation |
| :-------------------------------------------------: | :--------------: | :----------------------------------------------------------: |
| Mean | $\downarrow$ | mean rank of ground truth response in candidates |
| R@k | $\uparrow$ | ratio of ground truth response in the top-k ranked responses |
| Mean Reciprocal Rank (MRR) | $\uparrow$ | mean reciprocal rank of the ground truth response in the ranked responses |
| Normalized Discounted Cumulative Gain@k(NDCG@k) | $\uparrow$ | relevance score list, assigns 0-1 for 100 candidates responses based on semantic similarity with the ground truth responses |
#### Generate-based Metrics
| Metrics | Better indicator |
| :-----------------: | :--------------: |
| BLEU-k | $\uparrow$ |
| ROUGE-k/L | $\uparrow$ |
| Meteor | $\uparrow$ |
| CIDEr | $\uparrow$ |
| Embedding *Average* | $\uparrow$ |
| Embedding *Extrema* | $\uparrow$ |
| Embedding *Greedy* | $\uparrow$ |