---
dataset_info:
  features:
    - name: video_id
      dtype: string
    - name: video_path
      dtype: string
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: dimension
      dtype: string
  task_categories:
    - question-answering
    - video-understanding
  language:
    - en
  license: mit
  size_categories:
    - 10K<n<100K
  source_datasets:
    - original
  annotations_creators:
    - expert-generated
  multilinguality:
    - monolingual
---

# MarineEVT Dataset

[![Paper](https://img.shields.io/badge/Paper-ECCV%202026-blue)](https://marineevt.hkustvgd.com/)
[![Dataset Size](https://img.shields.io/badge/Size-20%2C000%20QA%20pairs-orange)]()
[![License](https://img.shields.io/badge/License-MIT-green)]()

**MarineEVT: Advancing Event-Centric Marine Video Understanding via Visual Tool Reasoning**

## 📖 Description

MarineEVT is a comprehensive event-centric dataset and benchmark for marine video understanding. It comprises **20,000 richly annotated underwater video question-answer pairs** spanning **20 fine-grained dimensions**, designed to support semantic, contextualized, spatial-temporal, and causal reasoning in marine environments.

The dataset addresses the challenge that informative events in marine videos are often **sparse**, **ephemeral**, and **unevenly distributed**, posing significant challenges for existing Video Language Models (VLMs).

## 📊 Dataset Statistics

| Metric | Value |
|--------|-------|
| **Total QA Pairs** | 20,000 |
| **Evaluation Set** | 2,000 pairs (reserved for testing) |
| **Dimensions** | 20 fine-grained categories |
| **Video Sources** | Underwater marine environments |

### Reasoning Types

The dataset covers five major reasoning categories:

1. **Semantic Reasoning** - Understanding what events happened and their semantic meaning
2. **Contextual Reasoning** - Identifying which entities are present in video events
3. **Spatial Reasoning** - Localizing where entities are in the underwater scene
4. **Temporal Reasoning** - Understanding when events occur and temporal dynamics
5. **Causal Reasoning** - Inferring causal relationships and why events occur

## 📁 Dataset Structure

```
MarineEVT/
├── CasualReasoning/
│   ├── Human-SpeciesCasualDynamics/
│   │   ├── train/
│   │   │   ├── videos/
│   │   │   └── multi_turn_data_ver2.json
│   │   └── test/
│   │       ├── videos/
│   │       └── multi_turn_data_ver2.json
│   ├── Inter-SpeciesCausalDynamics/
│   └── ReasonInference/
├── SpatialReasoning/
│   ├── [subdimension]/
│   │   ├── train/
│   │   └── test/
├── SemanticReasoning/
├── ContextualReasoning/
├── TemporalReasoning/
└── README.md
```

### Data Format

Each JSON file contains multi-turn QA pairs with the following structure:

```json
{
  "video_id": "zoDLceQg0J2U",
  "video_url": "",
  "question": "What is the marine animal doing?",
  "answer": "The animal is hunting for prey...",
  "question_task": "VideoQuestionAnswering",
  "dimension": "CasualReasoning",
  "subdimension": "Human-SpeciesCausalDynamics",
  "turns": [
    {
      "turn_id": 1,
      "id": 1,
      "visual_input": [...],
      "user_query": "...",
      "assistant_response": "..."
    }
  ]
}
```

## 📥 Download

The dataset is available for download at:

**Official Website**: [https://marineevt.hkustvgd.com/](https://marineevt.hkustvgd.com/)

**Hugging Face**: Use this dataset directly via the `datasets` library:

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("your-username/marineevt")

# Access training and test splits
train_data = dataset["train"]
test_data = dataset["test"]
```

## 🔧 Usage Example

```python
import json
from pathlib import Path

# Load training data
with open("CasualReasoning/Human-SpeciesCasualDynamics/train/multi_turn_data_ver2.json", "r") as f:
    data = json.load(f)

# Access a sample
sample = data[0]
print(f"Question: {sample['question']}")
print(f"Answer: {sample['answer']}")
print(f"Dimension: {sample['dimension']}")
```

## 📄 License

This dataset is released under the [MIT License](LICENSE).


## 📝 Citation

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

```bibtex
@inproceedings{to2026marineevt,
  title={{MarineEVT}: Advancing Event-Centric Marine Video Understanding via Visual Tool Reasoning},
  author={To, Tuan-An and Wong, Yuk-Kwan and Vu, Tuan-Anh and Zheng, Ziqiang and Yeung, Sai-Kit},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2026}
}
```

## 🔗 Links

- **Project Page**: [https://marineevt.hkustvgd.com/](https://marineevt.hkustvgd.com/)
- **Paper**: [arXiv](https://arxiv.org/) (coming soon)
- **Code**: [GitHub](https://github.com/ToTuanAn/MarineEVT/) (coming soon)