# Evaluating Model Perception of Color Illusions in Photorealistic Scenes

arXiv: https://arxiv.org/abs/2412.06184 · HTML: https://mlsanity.com/paper/2412.06184/

## Key facts

- Cited by 3 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.
- First posted on arXiv 9 Dec 2024.

## Placed in

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

## Built on by (most cited first, 3 of 3)

- [ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness](https://mlsanity.com/paper/2504.10514/) (2025)
- [ChromouVQA: Benchmarking Vision-Language Models under Chromatic Camouflaged Images](https://mlsanity.com/paper/2512.05137/) (2025)
- [Spatial Colour Mixing Illusions as a Perception Stress Test for Vision-Language Models](https://mlsanity.com/paper/2603.06141/) (2026)

## Abstract (arXiv)

We study the perception of color illusions by vision-language models. Color illusion, where a person's visual system perceives color differently from actual color, is well-studied in human vision. However, it remains underexplored whether vision-language models (VLMs), trained on large-scale human data, exhibit similar perceptual biases when confronted with such color illusions. We propose an automated framework for generating color illusion images, resulting in RCID (Realistic Color Illusion Dataset), a dataset of 19,000 realistic illusion images. Our experiments show that all studied VLMs exhibit perceptual biases similar human vision. Finally, we train a model to distinguish both human perception and actual pixel differences.