mlsanityData to 7 Oct 2026

Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering

arXiv 1902.07864 ↗First posted 21 Feb 2019cs.LG cs.AI cs.CVcited by 38

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Abstract (arXiv)

We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual question answering, our probabilistic formulation offers two key conceptual advantages over prior neural-symbolic models for VQA. Firstly, the programs generated by our model are more understandable while requiring lesser number of teaching examples. Secondly, we show that one can pose counterfactual scenarios to the model, to probe its beliefs on the programs that could lead to a specified answer given an image. Our results on the CLEVR and SHAPES datasets verify our hypotheses, showing that the model gets better program (and answer) prediction accuracy even in the low data regime, and allows one to probe the coherence and consistency of reasoning performed.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Feb 2019.

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Citing papers per year: 2021: 10; 2022: 13; 2023: 6; 2024: 5; 2025: 3; 2026: 1.

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  1. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  2. 2021MultiBench: Multiscale Benchmarks for Multimodal Representation Learningcited by 143
  3. 2022Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questionscited by 116
  4. 2021Neuro-Symbolic Artificial Intelligence: Current Trendscited by 54
  5. 2022MultiViz: Towards Visualizing and Understanding Multimodal Modelscited by 36
  6. 2022DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanationscited by 34
  7. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 34
  8. 2023Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directionscited by 25
  9. 2021Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsulescited by 22
  10. 2021Iterated learning for emergent systematicity in VQAcited by 21
  11. 2022DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detectioncited by 17
  12. 2023Grounded Object Centric Learningcited by 17
26 more
  1. 2023LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoningcited by 17
  2. 2024Softened Symbol Grounding for Neuro-symbolic Systemscited by 16
  3. 2024Visual Knowledge in the Big Model Era: Retrospect and Prospectcited by 16
  4. 2025Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoningcited by 14
  5. 2022A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answeringcited by 13
  6. 2021Domain-robust VQA with diverse datasets and methods but no target labelscited by 11
  7. 2023Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentationcited by 10
  8. 2024Neuro-Symbolic AI: Explainability, Challenges, and Future Trendscited by 10
  9. 2023Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questionscited by 9
  10. 2023V-LoL: A Diagnostic Dataset for Visual Logical Learningcited by 8
  11. 2022Neuro-Symbolic Learning: Principles and Applications in Ophthalmologycited by 7
  12. 2024Neural-Symbolic VideoQA: Learning Compositional Spatio-Temporal Reasoning for Real-world Video Question Answeringcited by 6
  13. 2022An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generationcited by 5
  14. 2025A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectivescited by 5
  15. 2021Neuro-Symbolic VQA: A review from the perspective of AGI desideratacited by 4
  16. 2021LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answeringcited by 4
  17. 2022Brainish: Formalizing A Multimodal Language for Intelligence and Consciousnesscited by 4
  18. 2024Closed Loop Interactive Embodied Reasoning for Robot Manipulationcited by 4
  19. 2022PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learningcited by 3
  20. 2022Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Modelscited by 3
  21. 2021Latent Variable Models for Visual Question Answeringcited by 2
  22. 2021How to Design Sample and Computationally Efficient VQA Modelscited by 2
  23. 2021NAAQA: A Neural Architecture for Acoustic Question Answeringcited by 2
  24. 2022Automated causal inference in application to randomized controlled clinical trialscited by 2
  25. 2026Distilling Neuro-Symbolic Programs into 3D Multi-modal LLMscited by 2
  26. 2025Augmented Vision-Language Models: A Systematic Reviewcited by 0