mlsanityData to 7 Oct 2026

Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review

arXiv 2207.00782 ↗First posted 2 Jul 2022cs.AI cs.HCcited by 2

Not on the map. Why?

Abstract (arXiv)

The intelligent dialogue system, aiming at communicating with humans harmoniously with natural language, is brilliant for promoting the advancement of human-machine interaction in the era of artificial intelligence. With the gradually complex human-computer interaction requirements (e.g., multimodal inputs, time sensitivity), it is difficult for traditional text-based dialogue system to meet the demands for more vivid and convenient interaction. Consequently, Visual Context Augmented Dialogue System (VAD), which has the potential to communicate with humans by perceiving and understanding multimodal information (i.e., visual context in images or videos, textual dialogue history), has become a predominant research paradigm. Benefiting from the consistency and complementarity between visual and textual context, VAD possesses the potential to generate engaging and context-aware responses. For depicting the development of VAD, we first characterize the concepts and unique features of VAD, and then present its generic system architecture to illustrate the system workflow. Subsequently, several research challenges and representative works are detailed investigated, followed by the summary of authoritative benchmarks. We conclude this paper by putting forward some open issues and promising research trends for VAD, e.g., the cognitive mechanisms of human-machine dialogue under cross-modal dialogue context, and knowledge-enhanced cross-modal semantic interaction.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 133 of its 207 references that are in the map. First posted on arXiv 2 Jul 2022.

Builds on

133 of 207 references.

References · 133

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  5. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  6. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  7. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  8. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  9. 2017Graph Attention Networkscited by 8,854
  10. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  11. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  12. 2015VQA: Visual Question Answeringcited by 2,489
38 more
  1. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  2. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
  3. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
  4. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
  5. 2020Knowledge Distillation: A Surveycited by 1,578
  6. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
  7. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
  8. 2016Pruning Filters for Efficient ConvNetscited by 1,129
  9. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
  10. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  11. 2016Modeling Context in Referring Expressionscited by 1,054
  12. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
  13. 2019VL-BERT: Pre-training of Generic Visual-Linguistic Representationscited by 954
  14. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  15. 2019VideoBERT: A Joint Model for Video and Language Representation Learningcited by 702
  16. 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 649
  17. 2018Joint Autoregressive and Hierarchical Priors for Learned Image Compressioncited by 642
  18. 2020X3D: Expanding Architectures for Efficient Video Recognitioncited by 635
  19. 2019Text Summarization with Pretrained Encoderscited by 553
  20. 2017Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?cited by 551
  21. 2016Hollywood in Homes: Crowdsourcing Data Collection for Activity Understandingcited by 535
  22. 2020Sparse R-CNN: End-to-End Object Detection with Learnable Proposalscited by 494
  23. 2016Visual Dialogcited by 377
  24. 2018AMC: AutoML for Model Compression and Acceleration on Mobile Devicescited by 375
  25. 2015Recent Advances in Convolutional Neural Networkscited by 360
  26. 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 298
  27. 2020Neural Scene Graphs for Dynamic Scenescited by 298
  28. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
  29. 2019A Survey of the Recent Architectures of Deep Convolutional Neural Networkscited by 211
  30. 2020ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graphcited by 211
  31. 2015Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Modelscited by 207
  32. 2020TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialoguecited by 185
  33. 2015The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systemscited by 172
  34. 2018Neural Approaches to Conversational AIcited by 164
  35. 2019Salient Object Detection in the Deep Learning Era: An In-Depth Surveycited by 163
  36. 2017A Survey on Dialogue Systems: Recent Advances and New Frontierscited by 162
  37. 2018Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalitiescited by 149
  38. 2019Challenges in Building Intelligent Open-domain Dialog Systemscited by 147

50 most cited of 133 All 133 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.