---
license: apache-2.0
task_categories:
- video-classification
- question-answering
- visual-question-answering
tags:
- perception-test
- video-qa
- multiple-choice
- video-understanding
size_categories:
- 1K<n<10K
viewer: true
---

# Perception Test MCQ Dataset

## Dataset Description

This dataset contains **1000 video question-answering entries** from the Perception Test dataset. Each entry includes a video and a multiple-choice question about the video content, testing various aspects of video understanding including object tracking, action recognition, and temporal reasoning.

## Dataset Structure

This dataset follows the VideoFolder format with the following structure:

```
dataset/
├── data/
│   ├── videos/
│   │   ├── video_XXX.mp4
│   │   └── ...
│   └── metadata.csv
└── README.md
```

### Metadata Format

The `metadata.csv` contains:
- `file_name`: Path to the video file (relative to split directory)
- `video_id`: Unique video identifier
- `question`: The question text
- `options`: JSON string containing multiple choice options
- `correct_answer`: The correct answer (available for 997/1000 entries)
- `question_type`: Type of question (typically "multiple choice")

## Dataset Statistics

- **Total QA pairs**: 1000
- **Unique videos**: 1000
- **Average questions per video**: 1.0
- **Entries with answers**: 997/1000 (99.7%)
- **Video format**: MP4

### Question Type Distribution

- `unknown`: 1000 questions

## Usage

### Loading the Dataset

```python
from datasets import load_dataset
import json

# Load the dataset
dataset = load_dataset("advaitgupta/perception_test_mcq")

# Access the data split
data = dataset['data']

# Example: Get first sample
sample = data[0]
print("Question:", sample['question'])
print("Options:", json.loads(sample['options']))
print("Correct Answer:", sample['correct_answer'])
print("Video:", sample['file_name'])
```

### Processing Videos and Questions

```python
import json
import cv2

# Load metadata
metadata = data.to_pandas()

# Process a video-question pair
sample = metadata.iloc[0]
video_path = sample['file_name']
question = sample['question']
options = json.loads(sample['options'])
correct_answer = sample['correct_answer']

print(f"Question: {question}")
for i, option in enumerate(options):
    print(f"{i+1}. {option}")
print(f"Correct Answer: {correct_answer}")

# Load and process video
cap = cv2.VideoCapture(video_path)
# ... your video processing code
```

## Task Types

This dataset covers various video understanding tasks:

### Object and Action Recognition
*"What ingredients did the person put in the bowl or on the plate?"*

### Temporal Reasoning
*"How many objects were put in the backpack throughout the video?"*

### Camera Motion Analysis
*"Is the camera moving or static?"*

### Spatial Understanding
*"Where is the person?"*

### Activity Recognition
*"What is the person preparing?"*

## Data Quality

- All video files have been validated to exist
- Questions are human-annotated from the Perception Test dataset
- Multiple choice format ensures consistent evaluation
- Fair distribution across videos (avg 1.0 questions per video)
- Correct answers provided for evaluation

## Example Entry

```json
{
  "file_name": "videos/video_10909.mp4",
  "video_id": "video_10909",
  "question": "Is the camera moving or static?",
  "options": ["I don't know", "moving", "static or shaking"],
  "correct_answer": "static or shaking",
  "question_type": "multiple choice"
}
```

## Citation

If you use this dataset, please cite the original Perception Test paper:

```bibtex
@article{perception-test-2022,
  title={Perception Test: A Diagnostic Benchmark for Multimodal Video Models},
  author={Pătrăucean, Viorica and others},
  journal={arXiv preprint arXiv:2211.13775},
  year={2022}
}
```

## License

This dataset is released under the Apache 2.0 license.