mlsanityData to 7 Oct 2026

Challenges and Prospects in Vision and Language Research

arXiv 1904.09317 ↗First posted 19 Apr 2019cs.LG cs.CL cs.CVcited by 24

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Abstract (arXiv)

Language grounded image understanding tasks have often been proposed as a method for evaluating progress in artificial intelligence. Ideally, these tasks should test a plethora of capabilities that integrate computer vision, reasoning, and natural language understanding. However, rather than behaving as visual Turing tests, recent studies have demonstrated state-of-the-art systems are achieving good performance through flaws in datasets and evaluation procedures. We review the current state of affairs and outline a path forward.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2019.

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Citing papers per year: 2021: 11; 2022: 5; 2023: 4; 2024: 2; 2025: 1; 2026: 1.

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  1. 2021VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomenacited by 134
  2. 2021Learning to Predict Visual Attributes in the Wildcited by 95
  3. 2021A Review on Explainability in Multimodal Deep Neural Netscited by 46
  4. 2022MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Taskscited by 42
  5. 2021Relation-aware Instance Refinement for Weakly Supervised Visual Groundingcited by 35
  6. 2024SOLO: A Single Transformer for Scalable Vision-Language Modelingcited by 35
  7. 2024Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchycited by 26
  8. 2025Explain Before You Answer: A Survey on Compositional Visual Reasoningcited by 26
  9. 2023SIESTA: Efficient Online Continual Learning with Sleepcited by 24
  10. 2023From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunitiescited by 24
  11. 2023Robust Visual Question Answering: Datasets, Methods, and Future Challengescited by 23
  12. 2021VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answeringcited by 16
12 more
  1. 2021Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventionscited by 15
  2. 2021Are Bias Mitigation Techniques for Deep Learning Effective?cited by 14
  3. 2022A survey on knowledge-enhanced multimodal learningcited by 11
  4. 2021Detecting Spurious Correlations with Sanity Tests for Artificial Intelligence Guided Radiology Systemscited by 10
  5. 2021Unanswerable Questions about Images and Textscited by 7
  6. 2022Every picture tells a story: Image-grounded controllable stylistic story generationcited by 5
  7. 2021TraVLR: Now You See It, Now You Don't! A Bimodal Dataset for Evaluating Visio-Linguistic Reasoningcited by 4
  8. 2022Vision-and-Language Pretrainingcited by 2
  9. 2023The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challengescited by 2
  10. 2026Step-Level Visual Grounding Faithfulness Predicts Out-of-Distribution Generalization in Long-Horizon Vision-Language Modelscited by 1
  11. 2021What is Multimodality?cited by 0
  12. 2022EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answeringcited by 0