README.md

---
license: mit
---

# HC-Bench

**HC-Bench** is a compact multi-part image benchmark for evaluating recognition and prompting robustness, especially in **hidden-content** scenes. It contains:

- **object/** — 56 base images and 56 *hidden* variants of the same lemmas, plus prompts and metadata.
- **text/** — 56 Latin/English and 56 Chinese lemma–description pairs with matching PNGs.
- **wild/** — 53 in-the-wild images for additional generalization checks.

---

## Repository structure

```

HC-Bench/
├─ object/
│  ├─ base/                     # 56 base images (7 types × 8 lemmas)
│  ├─ hidden/                   # 56 hidden-content variants (same lemmas)
│  ├─ image\_base.txt            # 7 types and their 8 lemmas each
│  ├─ image\_generate\_prompts.txt# per-lemma scene prompts used for generation
│  └─ lemmas\_descriptions.json  # \[{Type, Lemma, Description}] × 56
├─ text/
│  ├─ Latin/                    # 28 English PNGs
│  ├─ Chinese/                  # 28 Chinese PNGs
│  ├─ English\_text.json         # 56 entries (Type, Length, Rarity, Lemma, Description)
│  └─ Chinese\_text.json         # 56 entries (Type, Length, Rarity, Lemma, Description)
└─ wild/                        # 53 PNGs

````

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## Contents

### `object/`
- **`base/`**: Canonical image per lemma (e.g., `Apple.jpg`, `Einstein.png`).
- **`hidden/`**: Composite/camouflaged image for the *same* lemma set (e.g., `apple.png`, `einstein.png`).
- **`image_base.txt`**: The 7 high-level types and their 8 lemmas each (Humans, Species, Buildings, Cartoon, Furniture, Transports, Food).
- **`image_generate_prompts.txt`**: Per-lemma prompts used to compose/generate scenes (e.g., *“A monorail cutting through a futuristic city with elevated walkways”* for `notredame`).
- **`lemmas_descriptions.json`**: Minimal metadata with `{Type, Lemma, Description}` aligned 1:1 with the 56 lemmas.

### `text/`
- **`Latin/`** & **`Chinese/`**: 28 images each (total 56).
- **`English_text.json`** & **`Chinese_text.json`**: 56-entry lists pairing lemmas to descriptions in two languages.  
  (Note: The `English_text.json`/`Chinese_text.json` files include extra fields `Length` and `Rarity` for flexibility.)

### `wild/`
- 53 natural/urban scenes for robustness and transfer evaluation.

---

## Quick start (🤗 Datasets)

> HC-Bench uses the **ImageFolder**/“imagefolder” style. Class labels are inferred from directory names when present (e.g., `base`, `hidden`). If you prefer raw images without labels, pass `drop_labels=True`.

### Load **object/base** and **object/hidden**
```python
from datasets import load_dataset

base = load_dataset(
    "imagefolder",
    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/object/base/*",
    split="train",
    drop_labels=True,  # drop automatic label inference
)

hidden = load_dataset(
    "imagefolder",
    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/object/hidden/*",
    split="train",
    drop_labels=True,
)
````

### Load **wild/**

```python
wild = load_dataset(
    "imagefolder",
    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/wild/*",
    split="train",
    drop_labels=True,
)
```

### Load the **JSON** metadata (English/Chinese)

```python
from datasets import load_dataset

en = load_dataset(
    "json",
    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/text/English_text.json",
    split="train",
)
zh = load_dataset(
    "json",
    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/text/Chinese_text.json",
    split="train",
)
```

> Docs reference: `load_dataset` for JSON & files, and ImageFolder for image datasets.

---

## Pairing base/hidden with metadata

Filenames differ in casing/spaces between `base/` (`Apple.jpg`) and `hidden/` (`apple.png`). Use `object/lemmas_descriptions.json` as the canonical list of 56 lemmas and join by `Lemma`:

```python
import pandas as pd
from datasets import load_dataset

# 1) Canonical lemma list
lemmas = load_dataset(
    "json",
    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/object/lemmas_descriptions.json",
    split="train",
).to_pandas()

# 2) Build (lemma -> file) maps
def to_lemma(name):  # normalize filenames to lemma
    import re, os
    stem = os.path.splitext(os.path.basename(name))[0]
    return re.sub(r"\s+", "", stem).lower()

base_ds = load_dataset(
    "imagefolder",
    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/object/base/*",
    split="train",
    drop_labels=True,
)
hidden_ds = load_dataset(
    "imagefolder",
    data_files="https://huggingface.co/datasets/JohnnyZeppelin/HC-Bench/resolve/main/object/hidden/*",
    split="train",
    drop_labels=True,
)

import os
base_map  = {to_lemma(x["image"].filename): x["image"] for x in base_ds}
hidden_map= {to_lemma(x["image"].filename): x["image"] for x in hidden_ds}

# 3) Join
lemmas["base_image"]   = lemmas["Lemma"].apply(lambda L: base_map.get(L.lower()))
lemmas["hidden_image"] = lemmas["Lemma"].apply(lambda L: hidden_map.get(L.lower()))
```

---



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## Statistics

* `object/base`: 56 images
* `object/hidden`: 56 images
* `text/Latin`: 28 images
* `text/Chinese`: 28 images
* `wild`: 53 images

---

## Citation

If you use **HC-Bench**, please cite:

```bibtex
@misc{li2025semvinkadvancingvlmssemantic,
      title={SemVink: Advancing VLMs' Semantic Understanding of Optical Illusions via Visual Global Thinking}, 
      author={Sifan Li and Yujun Cai and Yiwei Wang},
      year={2025},
      eprint={2506.02803},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.02803}, 
}
```

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