# Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans?

arXiv: https://arxiv.org/abs/2311.00047 · HTML: https://mlsanity.com/paper/2311.00047/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 8 of 8)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://mlsanity.com/paper/2202.03052/) (2022)
- [Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks](https://mlsanity.com/paper/2206.08916/) (2022)
- [VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers](https://mlsanity.com/paper/2203.17247/) (2022)
- [What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance](https://mlsanity.com/paper/1912.06960/) (2019)

## Built on by (most cited first, 30 of 32)

- [Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference Under Ambiguities](https://mlsanity.com/paper/2410.17385/) (2024)
- [Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models](https://mlsanity.com/paper/2404.07983/) (2024)
- [Visual cognition in multimodal large language models](https://mlsanity.com/paper/2311.16093/) (2023)
- [IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models](https://mlsanity.com/paper/2403.15952/) (2024)
- [Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs](https://mlsanity.com/paper/2502.16435/) (2025)
- [IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models](https://mlsanity.com/paper/2501.00848/) (2025)
- [Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions](https://mlsanity.com/paper/2412.08169/) (2024)
- [Social Perception of Faces in a Vision-Language Model](https://mlsanity.com/paper/2408.14435/) (2024)
- [VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-Tuning](https://mlsanity.com/paper/2406.14056/) (2024)
- [The Art of Deception: Color Visual Illusions and Diffusion Models](https://mlsanity.com/paper/2412.10122/) (2024)
- [MindSet: Vision. A toolbox for testing DNNs on key psychological experiments](https://mlsanity.com/paper/2404.05290/) (2024)
- [Do VLMs Perceive or Recall? Probing Visual Perception vs. Memory with Classic Visual Illusions](https://mlsanity.com/paper/2601.22150/) (2026)
- [Seeing Is Believing? A Benchmark for Multimodal Large Language Models on Visual Illusions and Anomalies](https://mlsanity.com/paper/2602.01816/) (2026)
- [Evaluating Vision-Language Models on Bistable Images](https://mlsanity.com/paper/2405.19423/) (2024)
- [Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions](https://mlsanity.com/paper/2507.22617/) (2025)
- [Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models](https://mlsanity.com/paper/2502.15678/) (2025)
- [Visual Language Models show widespread visual deficits on neuropsychological tests](https://mlsanity.com/paper/2504.10786/) (2025)
- [Do Large Vision-Language Models Distinguish between the Actual and Apparent Features of Illusions?](https://mlsanity.com/paper/2506.05765/) (2025)
- [Illusions in Humans and AI: How Visual Perception Aligns and Diverges](https://mlsanity.com/paper/2508.12422/) (2025)
- [The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs](https://mlsanity.com/paper/2512.15949/) (2025)
- [SMSP: A Plug-and-Play Strategy of Multi-Scale Perception for MLLMs to Perceive Visual Illusions](https://mlsanity.com/paper/2603.23118/) (2026)
- [ChromouVQA: Benchmarking Vision-Language Models under Chromatic Camouflaged Images](https://mlsanity.com/paper/2512.05137/) (2025)
- [Now You See the Hate: Adaptive View Retrieval for Hidden Hateful Illusions](https://mlsanity.com/paper/2607.19061/) (2026)
- [A biological vision inspired framework for machine perception of abutting grating illusory contours](https://mlsanity.com/paper/2508.17254/) (2025)
- [Anchored, Not Graded: Vision-Language Models Fail at Slant-from-Texture Perception](https://mlsanity.com/paper/2606.06714/) (2026)
- [Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models](https://mlsanity.com/paper/2606.07641/) (2026)
- [Denoising Models Develop Human-Like Perceptual Illusion Representations Across Architectures](https://mlsanity.com/paper/2607.17138/) (2026)
- [Can LVLMs Uncover the Truth Behind Visual Illusions? An Analysis of Perceptual and Reasoning Capabilities](https://mlsanity.com/paper/2607.27747/) (2026)
- [More Accurate, Less Human: Gestalt Grouping in Vision Models](https://mlsanity.com/paper/2608.10195/) (2026)
- [Beyond What Meets the Eye: Unveiling Situational Illusions for Multimodal Large Language Models](https://mlsanity.com/paper/2608.22232/) (2026)

## Abstract (arXiv)

Vision-Language Models (VLMs) are trained on vast amounts of data captured by humans emulating our understanding of the world. However, known as visual illusions, human's perception of reality isn't always faithful to the physical world. This raises a key question: do VLMs have the similar kind of illusions as humans do, or do they faithfully learn to represent reality? To investigate this question, we build a dataset containing five types of visual illusions and formulate four tasks to examine visual illusions in state-of-the-art VLMs. Our findings have shown that although the overall alignment is low, larger models are closer to human perception and more susceptible to visual illusions. Our dataset and initial findings will promote a better understanding of visual illusions in humans and machines and provide a stepping stone for future computational models that can better align humans and machines in perceiving and communicating about the shared visual world. The code and data are available at https://github.com/vl-illusion/dataset.