metadata

base_model:
  - lmsys/vicuna-7b-v1.1
datasets:
  - MovieCORE/MovieCORE
  - Enxin/MovieChat-1K-test
license: mit
pipeline_tag: video-text-to-text

MovieCORE: COgnitive REasoning in Movies

A Video Question Answering Dataset for Probing Deeper Cognitive Understanding of Movie Content

📖 Overview

MovieCORE is a comprehensive video question answering (VQA) dataset specifically designed to evaluate and probe deeper cognitive understanding of movie content. Unlike traditional VQA datasets that focus on surface-level visual understanding, MovieCORE challenges models to demonstrate sophisticated reasoning about narrative structures, character development, thematic elements, and complex temporal relationships within cinematic content.

🗂️ Data Preparation

The MovieCORE dataset builds upon video content from MovieChat. To get started:

Video Data

Download the video files from MovieChat's HuggingFace repositories:

Training Data: MovieChat-1K Train
Test Data: MovieChat-1K Test

Annotations

Access our annotations on HuggingFace:

MovieCORE Annotations: 🤗 HuggingFace Dataset

Extract and organize the data according to your model's requirements, then use our annotations for evaluation.

🚀 Quick Start

Installation

git clone https://github.com/joslefaure/MovieCORE.git
cd MovieCORE

🎯 Baselines

We have provided the script to run HERMES (ICCV'25) on MovieCORE. Please check out the linked project.

📊 Evaluation Dimensions

MovieCORE employs a comprehensive multi-dimensional evaluation framework to assess model performance across different aspects of cognitive understanding:

Dimension	Description
🎯 Accuracy	Measures semantic similarity between predicted and ground truth answers
📋 Comprehensiveness	Assesses coverage of all key aspects mentioned in the ground truth
🧠 Depth	Evaluates level of reasoning and insight demonstrated in predictions
🔍 Evidence	Checks quality and relevance of supporting evidence provided
🔗 Coherence	Measures logical flow, organization, and clarity of responses

Each dimension provides unique insights into different cognitive capabilities required for deep video understanding.

💻 Usage

Evaluation Script

Evaluate your model's performance on MovieCORE using our evaluation script:

export OPENAI_API_KEY='your_openai_api_key'
python evaluate_moviecore.py --pred_path path/to/your/predictions.json

📝 Input Format

Your predictions should follow this JSON structure:

{
    "video_1.mp4": [
        {
            "question": "How does the video depict the unique adaptations of the species in the Sahara Desert, and what roles do these species play in their ecosystem?",
            "answer": "The ground truth answer.",
            "pred": "Your model's prediction.",
            "classification": "the question classification"
        },
        {
            "question": "The second question for video 1?",
            "answer": "The ground truth answer.",
            "pred": "Your model's prediction.",
            "classification": "the question classification"
        }
    ],
    "video_2.mp4": [
        {
            "question": "The only question for video 2",
            "answer": "The ground truth answer.",
            "pred": "Your model's prediction.",
            "classification": "the question classification"
        }
    ]
}

📈 Output

The evaluation script provides:

Overall scores across all dimensions
Classification-specific performance metrics
Detailed breakdowns for comprehensive analysis

📚 Citation

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

@misc{faure2025moviecorecognitivereasoningmovies,
      title={MovieCORE: COgnitive REasoning in Movies}, 
      author={Gueter Josmy Faure and Min-Hung Chen and Jia-Fong Yeh and Ying Cheng and Hung-Ting Su and Yung-Hao Tang and Shang-Hong Lai and Winston H. Hsu},
      year={2025},
      eprint={2508.19026},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.19026}, 
}

🤝 Contributing

We welcome contributions to MovieCORE! Please feel free to:

Report issues or bugs
Suggest improvements or new features
Submit baseline implementations
Provide feedback on the evaluation framework

📄 License

This dataset is provided under the MIT License. See LICENSE for more details.

🎬 Advancing Video Understanding Through Cognitive Evaluation 🎬

\ud83d\udcd6 Paper | \ud83e\udd17 Dataset | \ud83d\udcbb Code