| --- |
| 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: |
| |  |  |  | |
| |-------------------------|-------------------------|-------------------------| |
| ```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/ |
