| | --- |
| | dataset_info: |
| | features: |
| | - name: img_fn |
| | dtype: image |
| | - name: metadata_fn |
| | dtype: string |
| | - name: width |
| | dtype: int64 |
| | - name: height |
| | dtype: int64 |
| | - name: boxes |
| | dtype: string |
| | - name: objects |
| | dtype: string |
| | - name: segms |
| | dtype: string |
| | - name: keywords |
| | dtype: string |
| | - name: question_orig |
| | dtype: string |
| | - name: question |
| | dtype: string |
| | - name: answer_choices |
| | dtype: string |
| | - name: answer_orig |
| | dtype: string |
| | - name: answer_label |
| | dtype: int64 |
| | splits: |
| | - name: train |
| | num_bytes: 46088868 |
| | num_examples: 104 |
| | download_size: 40145967 |
| | dataset_size: 46088868 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | license: mit |
| | language: |
| | - en |
| | tags: |
| | - egypt |
| | - culture |
| | - arab |
| | - vision |
| | - language |
| | - LLM |
| | - VLM |
| | - VCR |
| | - common-sense-reasoning |
| | - multimodal |
| | --- |
| | |
| | # Dataset Summary |
| |
|
| | **EC-VCR (Egyptian Culture Visual Commonsense Reasoning)** is a multimodal benchmark designed to evaluate the cultural reasoning capabilities of Vision-Language Models (VLMs) within the specific context of **Egypt**. |
| |
|
| | Inspired by the methodology of **GD-VCR (Geo-Diverse Visual Commonsense Reasoning)**, this dataset moves beyond simple recognition ("What is this?") to high-order cognitive reasoning ("Why is this person performing this action?" or "What social event is taking place?"). It addresses the "cultural blind spot" in current AI models by focusing on scenarios unique to Egyptian daily life, traditions, and social dynamics. |
| |
|
| | This dataset is structured to support **Visual Question Answering (VQA)** and **Visual Commonsense Reasoning (VCR)** tasks, providing rich annotations including bounding boxes, object labels, and segmentation masks. |
| |
|
| | # Supported Tasks |
| |
|
| | * **Visual Commonsense Reasoning (VCR):** Answering "Why" and "How" questions that require external cultural knowledge. |
| | * **Visual Question Answering (VQA):** Standard question-answering based on image content. |
| | * **Object Detection:** Leveraging the provided bounding boxes and object tags. |
| |
|
| | # Dataset Structure |
| |
|
| | ## Data Instances |
| |
|
| | Each instance in the dataset represents a single question-answer pair associated with an image and its corresponding visual annotations. |
| |
|
| | ```python |
| | { |
| | "img_fn": "EC-VCR/1.jpg", |
| | "metadata_fn": "EC-VCR/1.json", |
| | "width": 1920, |
| | "height": 1080, |
| | "boxes": [[100, 200, 50, 80], [300, 400, 60, 90]], |
| | "objects": ["person", "car"], |
| | "segms": [[[100, 200, 105, 205, ...]], [[300, 400, ...]]], |
| | "keywords": ["wedding", "street", "celebration"], |
| | "question_orig": "Why are [person1] and [person2] wearing matching outfits?", |
| | "question": ["Why", "are", [0]", "and", "[1]", "wearing", "matching", "outfits", "?"], |
| | "answer_orig": [ |
| | "They are participating in a local festival procession.", |
| | "They are security guards for the building.", |
| | "They are part of a wedding entourage.", |
| | "They are casually walking to work." |
| | ], |
| | "answer_label": 2 |
| | } |
| | |
| | ``` |
| |
|
| | ### Data Fields |
| |
|
| | * **`img_fn`**: String. The relative path to the image file. |
| | * **`metadata_fn`**: String. The relative path to the source JSON containing segmentation and detailed metadata. |
| | * **`width`**: Integer. The width of the image in pixels. |
| | * **`height`**: Integer. The height of the image in pixels. |
| | * **`boxes`**: List of Lists. Bounding boxes for detected objects formatted as `[x1, y1, x2, y2]` (or `[x, y, w, h]` depending on your specific format). |
| | * **`objects`**: List of Strings. Class labels corresponding to the detected objects in `boxes`. |
| | * **`segms`**: List of Lists. Polygon points representing the segmentation masks for each object. |
| | * **`keywords`**: List of Strings. Categorical tags describing the scene context (e.g., "festival", "market"). |
| | * **`question_orig`**: String. The raw, natural language question string, often containing tags like `[person1]` to reference specific bounding boxes. |
| | * **`question`**: List of Strings. The tokenized or parsed version of the question, separating tags and punctuation for model input. |
| | * **`answer_orig`**: List of Strings. The list of possible answer choices (candidates) for the multiple-choice task. |
| | * **`answer_label`**: Integer. The zero-based index pointing to the correct answer in the `answer_orig` list. |
| | --- |
| | |
| | # Dataset Creation |
| | |
| | ## Curation Rationale |
| | |
| | Standard VCR datasets are heavily skewed toward Western contexts. As highlighted by the GD-VCR paper, models trained on these datasets fail to generalize to non-Western regions. EC-VCR fills this gap for Egypt, covering local customs, street scenes, and social interactions that global models often misinterpret. |
| | |
| | ## Source Data |
| | |
| | The images are collected and curated from movies, documentries and other online sources. |
| | |
| | |
| | (Note: You can add specific details here about your source, e.g., "Images were collected from Egyptian movies, TV series, and public domain cultural photography," similar to the GD-VCR methodology.) |
| | |
| | ## Annotation Process |
| | |
| | The dataset follows a VCR-style annotation pipeline: |
| | |
| | 1. **Object Detection:** Key objects are localized using bounding boxes and segmentation masks (Detectron2 package was used). |
| | 2. |
| | **Question Generation:** Questions are designed to be **high-order**, requiring the model to combine visual cues (detected objects) with implicit cultural knowledge. |
| | |
| | |
| | |
| | --- |
| |
|
| | # Usage |
| |
|
| | ## Loading the Dataset |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | # Load the dataset |
| | dataset = load_dataset("YourUsername/EC-VCR") |
| | |
| | # Access an example |
| | example = dataset['train'][0] |
| | image = example['image'] |
| | question = example['question'] |
| | annotations = example['boxes'] |
| | |
| | print(f"Question: {question}") |
| | image.show() |
| | |
| | ``` |
| |
|
| | --- |
| |
|
| | # Benchmarking & Evaluation |
| |
|
| | EC-VCR is designed to test **Cultural alignment**. High accuracy on this dataset indicates that a model understands: |
| |
|
| | 1. **Visual Recognition:** Identifying local objects (e.g., *Fanoos*). |
| | 2. **Social Reasoning:** Understanding the *intent* and *context* behind actions in an Egyptian setting (e.g., distinct gestures, seating arrangements, or ceremonial traditions). |
| |
|
| | --- |
| |
|
| | # Citation |
| |
|
| | If you use this dataset, please cite the following work: |
| |
|
| | ```bibtex |
| | @misc{gamil2025ecvcr, |
| | author = {Mohamed Gamil and Abdelrahman Elsayed and Abdelrahman Lila and Ahmed Gad and Hesham Abdelgawad and Mohamed Aref and Ahmed Fares}, |
| | title = {EC-VCR: A Visual Commonsense Reasoning Benchmark for Egyptian Culture}, |
| | year = {2026}, |
| | publisher = {Hugging Face}, |
| | howpublished = {\url{https://huggingface.co/datasets/CulTex-VLM/EG-VCR}} |
| | } |
| | |
| | ``` |
| |
|
| | **Methodology inspired by:** |
| |
|
| | ```bibtex |
| | @article{yin2021broaden, |
| | title={Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning}, |
| | author={Yin, Da and Li, Liunian Harold and Hu, Ziniu and Peng, Nanyun and Chang, Kai-Wei}, |
| | journal={arXiv preprint arXiv:2109.06860}, |
| | year={2021} |
| | } |
| | |
| | ``` |
