mlsanityData to 14 Oct 2026

Recent, rapid advancement in visual question answering architecture: a review

arXiv 2203.01322 ↗First posted 2 Mar 2022cs.CV cs.AI cs.CLcited by 5

Not on the map. Why?

Abstract (arXiv)

Understanding visual question answering is going to be crucial for numerous human activities. However, it presents major challenges at the heart of the artificial intelligence endeavor. This paper presents an update on the rapid advancements in visual question answering using images that have occurred in the last couple of years. Tremendous growth in research on improving visual question answering system architecture has been published recently, showing the importance of multimodal architectures. Several points on the benefits of visual question answering are mentioned in the review paper by Manmadhan et al. (2020), on which the present article builds, including subsequent updates in the field.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 41 of its 77 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

41 of 77 references.

References · 41

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  5. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  6. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  7. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  8. 2016Densely Connected Convolutional Networkscited by 6,720
  9. 2014Going Deeper with Convolutionscited by 4,241
  10. 2015VQA: Visual Question Answeringcited by 2,506
  11. 2018Deep contextualized word representationscited by 2,290
  12. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
29 more
  1. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,553
  2. 2020DocVQA: A Dataset for VQA on Document Imagescited by 1,317
  3. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,296
  4. 2019VL-BERT: Pre-training of Generic Visual-Linguistic Representationscited by 954
  5. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 461
  6. 2019Deep Modular Co-Attention Networks for Visual Question Answeringcited by 339
  7. 2015Visual7W: Grounded Question Answering in Imagescited by 318
  8. 2015Stacked Attention Networks for Image Question Answeringcited by 273
  9. 2020Counterfactual VQA: A Cause-Effect Look at Language Biascited by 266
  10. 2016Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Groundingcited by 243
  11. 2020In Defense of Grid Features for Visual Question Answeringcited by 155
  12. 2020Counterfactual Samples Synthesizing for Robust Visual Question Answeringcited by 153
  13. 2016Graph-Structured Representations for Visual Question Answeringcited by 123
  14. 2019Relation-Aware Graph Attention Network for Visual Question Answeringcited by 119
  15. 2014A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Inputcited by 112
  16. 2019Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editingcited by 108
  17. 2020TAP: Text-Aware Pre-training for Text-VQA and Text-Captioncited by 95
  18. 2017Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answeringcited by 79
  19. 2020Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?cited by 69
  20. 2015Where To Look: Focus Regions for Visual Question Answeringcited by 64
  21. 2020MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answeringcited by 63
  22. 2017Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answeringcited by 13
  23. 2018Question-Guided Hybrid Convolution for Visual Question Answeringcited by 12
  24. 2017Speech-Based Visual Question Answeringcited by 10
  25. 2019Towards Scalable and Reliable Capsule Networks for Challenging NLP Applicationscited by 10
  26. 2016A Focused Dynamic Attention Model for Visual Question Answeringcited by 9
  27. 2018Question Type Guided Attention in Visual Question Answeringcited by 8
  28. 2017Structured Attentions for Visual Question Answeringcited by 6
  29. 2017AI, Native Supercomputing and The Revival of Moore's Lawcited by 2

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 2.

5 papers.

  1. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 76
  2. 2024FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understandingcited by 27
  3. 2023Scalable Neural-Probabilistic Answer Set Programmingcited by 14
  4. 2023HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Languagecited by 12
  5. 2023RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logiccited by 8