IAmFuch/viet-cultural-vqa

🇻🇳 Vietnamese Cultural VQA Dataset

📖 Dataset Description

The Vietnamese Cultural VQA Dataset is a comprehensive multimodal dataset designed for Visual Question Answering (VQA) tasks focused on Vietnamese cultural heritage. This dataset aims to bridge the gap in understanding and preserving Vietnamese culture through AI-powered visual understanding and question answering.

🎯 Dataset Summary

• 📊 Total Images: 28,505 high-quality cultural images
• 💬 Total QA Pairs: 119,012 question-answer pairs
• 🌍 Languages: Vietnamese (primary), English (secondary)
• 🏛️ Categories: 12 major Vietnamese cultural domains
• 📜 License: Apache 2.0
• 📁 Format: JSON with image references
• 🎓 Quality: 97.5% high-quality annotations

🚀 Supported Tasks

• Visual Question Answering (VQA): Answer questions about Vietnamese cultural images
• Image Classification: Classify images into cultural categories
• Object Detection: Identify cultural objects and elements
• Cultural Understanding: Learn about Vietnamese traditions, customs, and heritage
• Multimodal Learning: Combine vision and language for cultural comprehension
• Cross-lingual Transfer: Vietnamese-English multimodal understanding

---

📂 Dataset Structure

💾 Data Instances

Each instance in the dataset contains rich annotations:

{
  "image_id": "kien_truc_chua_mot_cot_000001",
  "image_path": "images/kien_truc/chua_mot_cot/000001.jpg",
  "category": "kien_truc",
  "keyword": "chùa một cột",
  "image_analysis": {
    "overall_description": "Hình ảnh chùa Một Cột, kiến trúc Phật giáo độc đáo...",
    "main_objects": ["chùa", "cột đá", "mái cong", "hồ nước"],
    "visual_details": {
      "colors": ["nâu gỗ", "xanh rêu", "vàng", "xanh nước"],
      "materials": ["gỗ", "đá", "ngói", "nước"],
      "composition": "Trung tâm là chùa trên cột đá giữa hồ sen",
      "setting": "Môi trường văn hóa lịch sử, Hà Nội",
      "cultural_identification": "Kiến trúc Phật giáo Việt Nam thời Lý"
    }
  },
  "cultural_context": {
    "primary_cultural_objects": ["chùa Một Cột", "kiến trúc Lý"],
    "cultural_category": "Kiến trúc tôn giáo",
    "regional_significance": "Hà Nội, Bắc Bộ Việt Nam",
    "historical_context": "Xây dựng năm 1049 dưới triều vua Lý Thái Tông...",
    "modern_relevance": "Biểu tượng văn hóa Hà Nội, di sản quốc gia"
  },
  "questions": [
    {
      "question_id": 1,
      "question": "Đây là công trình kiến trúc nào?",
      "answer": "Chùa Một Cột",
      "detailed_explanation": "Chùa Một Cột là một trong những công trình kiến trúc độc đáo nhất...",
      "cultural_significance": "Biểu tượng văn hóa Việt Nam, di sản kiến trúc thời Lý",
      "difficulty": "easy",
      "question_type": "identification",
      "cognitive_level": "remember",
      "additional_context": {
        "origin": "Triều đại Lý, năm 1049",
        "usage": "Nơi thờ Phật, điểm tham quan văn hóa",
        "symbolism": "Hoa sen nở trên mặt nước - biểu tượng thanh tịnh",
        "regional_variations": "Độc nhất tại Hà Nội"
      }
    }
  ]
}

🔑 Data Fields

Field	Type	Description
image_id	string	Unique identifier for each image
image	Image	The image file (PIL Image object)
image_path	string	Relative path to the image
category	ClassLabel	One of 12 cultural categories
keyword	string	Primary cultural keyword/object
image_analysis	dict	Detailed image analysis
├─ overall_description	string	Comprehensive image description
├─ main_objects	list[string]	Key objects in the image
└─ visual_details	dict	Colors, materials, composition, setting, cultural ID
cultural_context	dict	Cultural background information
├─ primary_cultural_objects	list[string]	Main cultural elements
├─ cultural_category	string	Subcategory classification
├─ regional_significance	string	Geographic/regional context
├─ historical_context	string	Historical background
└─ modern_relevance	string	Contemporary significance
questions	list[dict]	List of Q&A pairs
├─ question_id	int	Question identifier
├─ question	string	The question text
├─ answer	string	The answer text
├─ detailed_explanation	string	Comprehensive explanation
├─ cultural_significance	string	Cultural importance
├─ difficulty	string	easy, medium, or hard
├─ question_type	string	identification, description, cultural, analysis, comparison
├─ cognitive_level	string	remember, understand, apply, analyze, evaluate (Bloom's Taxonomy)
└─ additional_context	dict	origin, usage, symbolism, regional_variations

📊 Data Splits

Split	Samples	QA Pairs	Percentage	Size
Train	18,806	~89,400	75%	~195 MB
Validation	3,761	~17,900	15%	~24 MB
Test	2,507	~11,900	10%	~25 MB
Total	25,074	119,012	100%	~244 MB

---

🏛️ Dataset Categories

The dataset covers 12 major Vietnamese cultural domains:

#	Category	Vietnamese Name	Description	Images	Keywords
1	Architecture	Kiến trúc	Temples, pagodas, traditional houses, palaces	2,979	chùa, đền, nhà rường, lăng
2	Cuisine	Ẩm thực	Traditional dishes, street food, ingredients	~2,500	phở, bánh mì, bún, chả
3	Landscapes	Phong cảnh	Natural heritage, scenic spots, landmarks	2,929	Hạ Long, Sapa, đồng ruộng
4	Clothing	Trang phục	Áo dài, ethnic costumes, traditional attire	2,485	áo dài, áo tứ thân, trang phục dân tộc
5	Daily Life	Đời sống hàng ngày	Markets, street scenes, everyday activities	2,493	chợ, phố cổ, sinh hoạt
6	Folk Culture	Văn hóa dân gian	Water puppetry, folk arts, traditional performances	1,969	múa rối nước, hát chèo, ca trù
7	Festivals	Lễ hội	Traditional celebrations, ceremonies, rituals	2,387	Tết, lễ hội đền, rước kiệu
8	Traditional Games	Trò chơi dân gian	Folk games, children's games	2,469	đánh đu, kéo co, ô ăn quan
9	Traditional Sports	Thể thao truyền thống	Martial arts, traditional sports	2,439	võ cổ truyền, đua thuyền
10	Handicrafts	Thủ công mỹ nghệ	Ceramics, lacquerware, silk, bamboo crafts	1,986	gốm sứ, sơn mài, tơ tằm
11	Music	Nhạc cụ	Traditional Vietnamese instruments	1,453	đàn tranh, đàn bầu, sáo trúc
12	Transportation	Giao thông	Cyclos, sampans, traditional vehicles	1,485	xích lô, thuyền, ghe

---

📈 Dataset Statistics

🎯 Question Analysis

Difficulty Distribution:

• 🟢 Easy: 25,162 (21.1%) - Basic identification and recognition
• 🟡 Medium: 46,441 (39.0%) - Description and understanding
• 🔴 Hard: 47,409 (39.8%) - Analysis and cultural insight

Question Types:

• 🔍 Identification: 24,892 (20.9%) - "What is this?"
• 📝 Description: 22,252 (18.7%) - "Describe the image"
• 🏛️ Cultural: 23,969 (20.1%) - "What is the cultural significance?"
• 🧠 Analysis: 23,982 (20.1%) - "Why is this important?"
• ⚖️ Comparison: 23,889 (20.1%) - "How does this compare?"

Cognitive Levels (Bloom's Taxonomy):

• 💭 Remember: 24,842 (20.9%) - Recall facts
• 🧩 Understand: 25,794 (21.7%) - Explain concepts
• 🛠️ Apply: 19,747 (16.6%) - Use knowledge
• 🔬 Analyze: 26,564 (22.3%) - Break down info
• ⭐ Evaluate: 22,018 (18.5%) - Make judgments

✅ Quality Metrics

• High Quality Annotations: 24,446 samples (97.5%)
• AI-Assisted Annotations: 628 samples (2.5%)
• Average Explanation Length: 295 characters
• Average Questions per Image: 4.75
• Cultural Expert Validation: Yes

---

💻 Usage

🔧 Installation

pip install datasets pillow

📥 Load the Dataset

from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("Dangindev/viet-cultural-vqa")

# Load specific splits
train_data = load_dataset("Dangindev/viet-cultural-vqa", split="train")
val_data = load_dataset("Dangindev/viet-cultural-vqa", split="validation")
test_data = load_dataset("Dangindev/viet-cultural-vqa", split="test")

# Access a sample
sample = dataset["train"][0]
print(f"Image ID: {sample['image_id']}")
print(f"Category: {sample['category']}")
print(f"Question: {sample['questions'][0]['question']}")
print(f"Answer: {sample['questions'][0]['answer']}")

# Display image
sample['image'].show()

🔍 Filtering by Category

# Filter architecture images
architecture = dataset["train"].filter(
    lambda x: x["category"] == 1  # kien_truc
)

# Filter by difficulty
hard_questions = dataset["train"].filter(
    lambda x: any(q["difficulty"] == "hard" for q in x["questions"])
)

# Filter by question type
cultural_questions = dataset["train"].filter(
    lambda x: any(q["question_type"] == "cultural" for q in x["questions"])
)

🤖 Training a VQA Model

from transformers import ViltProcessor, ViltForQuestionAnswering
from torch.utils.data import DataLoader
import torch

# Load model and processor
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

# Prepare dataset
def preprocess_function(examples):
    images = []
    questions = []
    
    for img, qs in zip(examples["image"], examples["questions"]):
        for q in qs:
            images.append(img)
            questions.append(q["question"])
    
    encoding = processor(images, questions, padding="max_length", truncation=True, return_tensors="pt")
    return encoding

# Process dataset
processed_dataset = dataset["train"].map(
    preprocess_function, 
    batched=True, 
    remove_columns=dataset["train"].column_names
)

# Create dataloader
train_dataloader = DataLoader(processed_dataset, batch_size=8, shuffle=True)

# Training loop (simplified)
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)

model.train()
for batch in train_dataloader:
    outputs = model(**batch)
    loss = outputs.loss
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

📊 Data Analysis

import pandas as pd

# Analyze category distribution
categories = [sample["category"] for sample in dataset["train"]]
pd.Series(categories).value_counts()

# Analyze question difficulty
difficulties = []
for sample in dataset["train"]:
    for q in sample["questions"]:
        difficulties.append(q["difficulty"])
pd.Series(difficulties).value_counts()

# Average questions per image
avg_questions = sum(len(s["questions"]) for s in dataset["train"]) / len(dataset["train"])
print(f"Average questions per image: {avg_questions:.2f}")

---

🛠️ Data Collection and Annotation

📸 Image Collection

Images were collected from multiple sources:

• ✅ Public domain Vietnamese cultural archives
• ✅ Creative Commons licensed photographs
• ✅ Curated web crawling with cultural keywords
• ✅ Collaborative contributions from cultural experts
• ✅ Vietnamese tourism and heritage websites

✍️ Annotation Process

1. Image Analysis (Automated)

   • Google Gemini Vision API for initial analysis
   • Object detection and scene understanding

2. Cultural Context (Expert-guided)

   • Vietnamese cultural experts review and enrich annotations
   • Historical and regional context added

3. Question Generation (AI + Human)

   • AI-assisted question generation with templates
   • Human review and refinement
   • Multiple cognitive levels (Bloom's Taxonomy)

4. Quality Control (Multi-stage)

   • Automated validation checks
   • Expert review of samples
   • Community feedback integration

5. Cultural Verification

   • Review by Vietnamese cultural experts
   • Regional variations documented
   • Historical accuracy ensured

📋 Annotation Guidelines

• ✅ Questions cover multiple cognitive levels
• ✅ Answers include detailed cultural explanations
• ✅ Focus on authenticity and cultural accuracy
• ✅ Bilingual support (Vietnamese primary)
• ✅ Regional diversity representation
• ✅ Respect for cultural sensitivity

---

🤝 Ethical Considerations

🌏 Cultural Sensitivity

• All images and annotations respect Vietnamese cultural heritage
• Traditional knowledge presented with appropriate context
• Regional variations acknowledged and documented
• No stereotyping or cultural appropriation
• Consultation with Vietnamese cultural experts

🔒 Privacy

• No personal identifying information in images
• Public spaces and cultural artifacts only
• Consent obtained where applicable
• No sensitive or private cultural practices

⚖️ Bias Mitigation

• Balanced representation across regions (North, Central, South Vietnam)
• Diverse cultural categories to avoid stereotyping
• Multiple perspectives on cultural practices
• Gender and age diversity in depicted subjects
• Urban and rural representation

---

⚠️ Limitations

• Geographic Coverage: Some remote regions may be underrepresented
• Historical Depth: Focus on contemporary and recent culture (post-20th century)
• Language: Primary content in Vietnamese; English translations may vary in quality
• Automation: Some annotations generated by AI and may contain minor errors
• Cultural Nuance: Complex cultural concepts may be simplified for accessibility
• Image Quality: Varies based on source (mostly high quality, some moderate)
• Temporal Coverage: Modern images; historical period images limited

---

📚 Citation

If you use this dataset in your research, please cite:

@misc{VietMEAgent,
      title={VietMEAgent: Culturally-Aware Few-Shot Multimodal Explanation for Vietnamese Visual Question Answering}, 
      author={Hai-Dang Nguyen and Minh-Anh Dang and Minh-Tan Le and Minh-Tuan Le},
      year={2025},
      eprint={2511.09058},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.09058}, 
}

---

📄 License

This dataset is licensed under the Apache License 2.0.

✅ You are free to:

• Share: copy and redistribute the material
• Adapt: remix, transform, and build upon the material
• Commercial use: use the material for commercial purposes

⚠️ Under the following terms:

• Attribution: provide appropriate credit and indicate changes
• No additional restrictions: no legal/technological measures that restrict others

See LICENSE for full details.

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👥 Dataset Maintainers

• Team: VietMeAgent Team
• Contact: Dangindev on Hugging Face
• Repository: https://huggingface.co/datasets/Dangindev/viet-cultural-vqa
• Issues: Please report issues on the repository

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📝 Changelog

Version 1.0.0 (October 2024)

• ✨ Initial release
• 📊 28,505 images across 12 cultural categories
• 💬 119,012 question-answer pairs
• 🏛️ Multi-level annotations with rich cultural context
• 📂 Train/validation/test splits (75/15/10)
• 🔧 HuggingFace datasets integration
• 📖 Comprehensive documentation

---

🙏 Acknowledgments

We thank:

• 🇻🇳 Vietnamese cultural experts for validation and guidance
• 🌐 Open-source community for tools and frameworks
• 🤗 Hugging Face for hosting and infrastructure
• 👥 Contributors who helped curate and validate the dataset
• 🏛️ Vietnamese heritage organizations for support
• 📚 Academic institutions for collaboration

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🔮 Future Work

• 🌟 Expand to more granular subcategories
• ⏳ Add temporal evolution tracking (historical changes)
• 🔊 Include audio descriptions for accessibility
• 🌍 Multilingual expansion (French, Chinese, Japanese)
• 🤝 Interactive annotation tool for community contributions
• 📹 Video annotations for dynamic cultural practices
• 🗺️ Geographic metadata and mapping
• 🎓 Educational curriculum integration

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🏷️ Keywords

Vietnamese culture • Visual Question Answering • Multimodal Learning • Cultural Heritage • Traditional Culture • Southeast Asian AI • Cultural Understanding • VQA Dataset • Image Classification • Vietnamese Language • Cultural Preservation • AI for Heritage • Multimodal Dataset • Computer Vision • Natural Language Processing

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⭐ If you find this dataset useful, please give it a star and cite it in your work!