---
license: cc-by-4.0
task_categories:
  - zero-shot-image-classification
  - image-classification
  - visual-question-answering
tags:
  - vision-language-models
  - cue-conflict
  - color-hybrid-illusions
  - factorized-diffusion
  - multimodal-evaluation
  - perceptual-bias
  - optical-illusions
  - diffusion-models
size_categories:
  - n<1K
---

# Color Hybrid Illusions Dataset

A benchmark dataset of **177 image pairs** for studying how vision-language models (VLMs) resolve conflicting visual cues. Each image depicts one entity in **color** and a different entity in **grayscale**, created using [Factorized Diffusion](https://arxiv.org/abs/2407.11900).

## Overview

When you view a color hybrid image in full color, you see one object (e.g., a bird). When you convert it to grayscale, a different object emerges (e.g., a flower). This dataset uses that conflict to test whether VLMs rely more on chromatic (color) or luminance (grayscale/shape) cues for object recognition.

**Key finding:** Across 11 VLMs and 3,894 predictions, most models exhibit **grayscale bias** (avg gray accuracy 0.681 vs. color accuracy 0.554), suggesting VLMs generally privilege shape and luminance structure over color information.

## How the Dataset Was Generated

Images were generated using **Factorized Diffusion** ([Geng et al., ECCV 2024](https://arxiv.org/abs/2407.11900)), which decomposes a diffusion model's denoising process into separate linear components — in this case, grayscale (luminance) and color (chrominance) channels. Each component is conditioned on a different text prompt during sampling, producing a single image that depicts one object in color and a different object in grayscale structure.

The underlying diffusion model is **DeepFloyd IF**, a pixel-based cascaded diffusion pipeline that generates 1024×1024 images. Text prompts are encoded with a **T5 text encoder** and guide the denoising process across both views.

**Pipeline:**
1. **Prompt pairing** — Each image pair is generated from two prompts: one describing a grayscale object (e.g., *"a shaded sketch of a lily"*) and one describing a color object (e.g., *"a vivid poster of a finch"*).
2. **Factorized sampling** — The diffusion model denoises both the grayscale and color components simultaneously, each conditioned on its respective prompt.
3. **Human auditing** — From an initial pool of 2,400 generated pairs, each image was manually reviewed and assigned a quality tier. Only pairs that successfully produced a visible illusion were retained, resulting in the final set of **177 pairs**.

## Dataset Structure

- **`dataset.json`** — Metadata for all 177 pairs, including prompts, object labels, and quality tiers.
- **`images/`** — 354 PNG images (one color `c` + one grayscale `g` per pair).

### Naming Convention

Images are named `{number}c.png` (color view) and `{number}g.png` (grayscale view), zero-padded to 4 digits. For example, pair #98 → `0098c.png` and `0098g.png`.

### Metadata Fields

| Field | Description |
|---|---|
| `number` | Image pair ID |
| `greyscale` | Prompt used for the grayscale component |
| `color` | Prompt used for the color component |
| `quality` | Human-rated quality tier: **L** (low), **M** (medium), **H** (high) |
| `grey_object` | Ground-truth object label for the grayscale view |
| `color_object` | Ground-truth object label for the color view |

### Quality Tiers

Quality tiers assess how well each generated illusion decouples luminance structure from chromatic information:

- **H (High):** Clear, drastic difference between entities across views — the illusion is immediately apparent
- **M (Medium):** Moderate distinction between entities
- **L (Low):** Less distinction; both views may partially resemble each other

## Example

| Grayscale View → "flower" | Color View → "bird" |
|:---:|:---:|
| `0098g.png` | `0098c.png` |

## Benchmark Results

### Per-Model Performance (Forced-Choice)

| Model | Overall Acc. | Gray Acc. | Color Acc. | Δ | Bias |
|---|---|---|---|---|---|
| ALIGN | 0.701 | 0.785 | 0.616 | +0.169 | Gray |
| SigLIP | 0.684 | 0.746 | 0.621 | +0.124 | Gray |
| LLaVA-1.6 | 0.667 | 0.802 | 0.531 | +0.271 | Gray |
| SmolVLM | 0.655 | 0.729 | 0.582 | +0.147 | Gray |
| Qwen2-VL | 0.653 | 0.695 | 0.610 | +0.085 | Gray |
| GPT-4o-mini | 0.644 | 0.689 | 0.599 | +0.090 | Gray |
| LLaVA-1.5 | 0.633 | 0.757 | 0.508 | +0.249 | Gray |
| CLIP | 0.630 | 0.802 | 0.458 | +0.345 | Gray |
| GPT-5.5 | 0.540 | 0.497 | 0.584 | −0.087 | Color |
| BLIP-2 | 0.500 | 0.435 | 0.565 | −0.130 | Color |
| Moondream2 | 0.483 | 0.548 | 0.418 | +0.130 | Gray |

### Architecture Families

| Family | Models | Avg. Accuracy |
|---|---|---|
| Contrastive | CLIP, ALIGN, SigLIP | 0.671 |
| Generative (Q-Former) | BLIP-2 | 0.500 |
| Instruction-tuned LLM | LLaVA-1.5, LLaVA-1.6, Qwen2-VL | 0.651 |
| Compact VLM | SmolVLM, Moondream2 | 0.569 |
| Proprietary API | GPT-4o-mini, GPT-5.5 | 0.592 |

## Intended Use

This dataset is intended for:

- **Evaluating VLM cue arbitration** — testing whether models rely on shape/luminance or color when the two conflict
- **Benchmarking multimodal robustness** — assessing model performance on perceptually ambiguous inputs
- **Studying representation bias** — understanding how training objectives (contrastive, generative, instruction-tuned) influence visual feature weighting

## Citation

```bibtex
@misc{li2026entityrecognition,
  title={Entity Recognition with Vision Language Models on Diffusion-Based Color Hybrid Illusions},
  author={Bill Li and Paul Junver Soriano and Rahul Koonantavida},
  year={2026},
  institution={San Jos\'{e} State University}
}
```

## Links

- **Project Website:** [hybrid-color-images.vercel.app](https://hybrid-color-images.vercel.app/)
- **Factorized Diffusion Paper:** [Geng et al., ECCV 2024](https://arxiv.org/abs/2407.11900)
- **Visual Anagrams Paper:** [Geng et al., CVPR 2024](https://arxiv.org/abs/2311.17919)