README.md

---
configs:
- config_name: Color
  data_files: data/CLEVR_mini_attribution_color.json
- config_name: Count
  data_files: data/CLEVR_mini_attribution_count.json
- config_name: Material
  data_files: data/CLEVR_mini_attribution_material.json
license: mit
size_categories:
- 10K<n<80K
---

# CLEVR-Per Dataset README

## Overview
**CLEVR-Per** is a cross-modal dataset integrating images, baseline (true) captions, and permuted (false) captions to facilitate research on "compositional understanding" tasks in vision-language models.
The dataset is designed to explore VLMs' ability to understand semantic changes in vision and textual entities by swapping attributes vocabulary, creating different semantic expressions.
CLEVR-Per contains three subsets: **ColPer** (color semantics permutation), **CntPer** (quantity semantics permutation), and **MatPer** (material semantics permutation), each focusing on specific types of semantic transformations.

## Supported Tasks
**CLEVR-Per** is suitable for the following tasks:
- **Compositional Understanding Evaluation**: Assessing the model's ability to understand semantic changes. The model selects the most relevant description from baseline caption and permuted caption for a given image, testing its compositional understanding.
- **Adversarial Sample Generation**: Researchers can generate diverse adversarial samples using baseline and permuted caption for contrastive training, improving the model's ability to detect erroneous semantics.

<figure>
  <img src="./introduction.png" alt="compositional understanding" style="width: 700px;" >
  <figcaption>Fig 1. Compositional Understanding: Clip model chose the disturbed incorrect caption, confused in understanding the relationship between entities and attributes.</figcaption>
</figure>

## Data Source and Statistics

The **CLEVR-Per** dataset is derived from **CLEVR** [1], with all baseline and permuted captions generated based on predefined rules and templates.

### Dataset Statistics and Example

| Subset  | Number of Images | Baseline Captions | Permuted Captions |
|---------|------------------|--------------------------|-----------------------|
| **ColPer** | 4000 | 16905 | 16905 |
| **CntPer** | 4000 | 7292  | 7292  |
| **MatPer** | 4000 | 10589 | 10589 |

### Data Structure

| Field Name         | Data Type   | Description                                |
|--------------------|-------------|--------------------------------------------|
| **image_id**        | string      | Identifier for the image sample           |
| **image_path**      | string      | Path or URL of the image sample           |
| **obj1_name**       | string      | Name of the first entity in caption       |
| **obj2_name**       | string      | Name of the second entity in caption      |
| **true_caption**    | string      | Baseline caption describing the image     |
| **false_caption**   | string      | Caption formed by swapping attributes in the baseline caption   |
| **attributes**      | list        | List of attributes related to both entities   |
| **id**              | int         | Unique Identifier for the sample        |

### Example

| Field Name         | Data Type   | Description                                |
|--------------------|-------------|--------------------------------------------|
| **image_id**        | string      | `000001`     |
| **image_path**      | string      | `CLEVR_mini_000001.png`             |
| **obj1_name**       | string      | `sphere`          |
| **obj2_name**       | string      | `cube`             |
| **true_caption**    | string      | `"yellow sphere and purple cube."` |
| **false_caption**   | string      | `"purple sphere and yellow cube."` |
| **attributes**      | list        | `["yellow", "purple"]` |
| **id**              | int         | `100` |

## Build Your Dataset with Simple Code
Using simple code, you can build your richer and more diverse negative sample dataset obased on CLEVR-Per.

For example:
- "is This a test true caption"
- "caption This is a true test"
- "a test This is true caption"
```python
import random

def generate_negative_samples(true_caption, num_samples=3):
    """
    Efficiently generates negative samples by shuffling the word order of a given caption.
    
    Args:
        true_caption (str): The original caption.
        num_samples (int): The number of negative samples to generate (default: 3).
    
    Returns:
        list: A list of generated negative samples.
    """
    words = true_caption.split()
    word_count = len(words)
    
    # Limit the number of unique permutations to the factorial of word count if needed
    max_unique_permutations = min(num_samples, len(set(random.sample(words, word_count)) for _ in range(num_samples)))
    
    # Use a set to avoid duplicates
    negative_samples = {
        ' '.join(random.sample(words, word_count))
        for _ in range(max_unique_permutations * 10)  # Extra sampling to reduce duplicates
    }
    
    # Convert to list and limit the final count
    return list(negative_samples)[:num_samples]

# Example usage
true_caption = "This is a test true caption"
negative_samples = generate_negative_samples(true_caption)
print(negative_samples)
```

For example:
| ![Image 1](./shuffled_image_1.png) | ![Image 2](./shuffled_image_2.png) | ![Image 3](./shuffled_image_3.png) |
|-------------------------|-------------------------|-------------------------|
```python
import random
from PIL import Image
import os

def generate_shuffled_images(image_path, grid_size=(4, 4), num_images=3, output_dir="shuffled_images"):
    """
    Efficiently generates multiple images by shuffling blocks of the original image.

    Args:
        image_path (str): Path to the input image.
        grid_size (tuple): Number of rows and columns to split the image (e.g., (4, 4)).
        num_images (int): Number of shuffled images to generate.
        output_dir (str): Directory to save the generated images.

    Returns:
        None
    """
    # Load the image and determine dimensions
    image = Image.open(image_path)
    img_width, img_height = image.size
    rows, cols = grid_size
    block_width, block_height = img_width // cols, img_height // rows

    # Ensure output directory exists
    os.makedirs(output_dir, exist_ok=True)

    # Pre-split the image into blocks (avoiding redundant operations)
    blocks = [
        image.crop((c * block_width, r * block_height, (c + 1) * block_width, (r + 1) * block_height))
        for r in range(rows) for c in range(cols)
    ]

    # Generate shuffled images
    for i in range(num_images):
        random.shuffle(blocks)  # In-place shuffle for better efficiency
        shuffled_image = Image.new('RGB', (img_width, img_height))
        for idx, block in enumerate(blocks):
            r, c = divmod(idx, cols)
            shuffled_image.paste(block, (c * block_width, r * block_height))
        
        # Save the shuffled image
        output_path = os.path.join(output_dir, f"shuffled_image_{i + 1}.png")
        shuffled_image.save(output_path)
        print(f"Generated: {output_path}")

# Example usage
input_image_path = "/mnt/data/2276b7b0-17f8-458c-bbc0-24abf62ab34b.png"
generate_shuffled_images(image_path=input_image_path, grid_size=(4, 4), num_images=3)
```


## References

[1]. **CLEVR**. CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning. *Justin Johnson*, 2017. https://cs.stanford.edu/people/jcjohns/clevr/.


### If you use this dataset, please cite it as follows. My homepage https://tongli97.github.io/.

<pre>
@misc{cleverper_dataset,
  author       = {Tong Li, Guodao Sun*, Xueqian Zheng, Qi Jiang, Wang Xia, Xu Tan, Haidong Gao, Jingwei Tang, Yunchao Wang, Haixia Wang, Ronghua Liang},
  title        = {CompoVis: Is Cross-modal Semantic Alignment of CLIP Optimal? A Visual Analysis Attempt},
  year         = {2026},
  publisher    = {IEEE Transactions on Multimedia},
  DOI          = {10.1109/TMM.2026.3660158}
  howpublished = {\url{https://huggingface.co/datasets/guodaosun/CompoVIS}},
}
</pre>

## Contact Information

- **Author**: Tong Li (李童)
- **Email**: litong@zjut.edu.cn
- **Project Page**: https://tongli97.github.io/