Video-Skill-CoT

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Video-Skill-CoT

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Code for "Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning [EMNLP 2025 Findings]"

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# 🛠️ Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning

[![Project Website](https://img.shields.io/badge/Project-Website-blue)](https://video-skill-cot.github.io/)  [![arXiv](https://img.shields.io/badge/arXiv-2506.03525-b31b1b.svg)](https://arxiv.org/pdf/2506.03525)

#### [Daeun Lee*](https://daeunni.github.io/), [Jaehong Yoon*](https://jaehong31.github.io/), [Jaemin Cho](https://j-min.io), [Mohit Bansal](https://www.cs.unc.edu/~mbansal/)    
#### EMNLP 2025 Findings

> 📌 **`TL;DR`** Video-Skill-CoT is a skill-aware CoT reasoning framework that constructs **domain-specific multi-step rationales** and trains expert modules for adaptive video understanding.

## 🔧 Setup

### OpenAI/Gemini API Setup 
Our Video-Skill-CoT is based on openai/gemini api, so you need to setup your Azure OpenAI/Gemini API config in the below files. 
You can set your own API infomation in `./skill_cot_generation/config.ini`.

```python
[openai]
azure_endpoint = your endpoint   
api_key = your key 
api_version = your version 
[gemini]
gemini_api_key = your gemini_api_key
gemini_application_credentials = your credentials 
```

### Download datasets 
Please locate all downloaded datasets in the  `./video_instruction_datasets` directory. The data structure will like below: 
```bash
./video_instruction_datasets
    ├── cinepile
    ├── ET_164k
    ├── VSI-Bench
```

## 🔩 Skill-CoT Generation 
Based on above video understanding datasets, you can generate skill-cot as follows: 
```bash
# [Step 1] Skill clustering 
python ./skill_cot_generation/clustering.py --dataset='cine'

# [Step 2] Skill-CoT generation 
python ./skill_cot_generation/skill_cot_generation.py --dataset='cine' --mode='skill_cot'

# [Step 3] Skill-CoT filtering   
python ./skill_cot_generation/filtering.py --dataset='cine'  
```

## 📝 TODO List
- [ ] Release Multi-LoRA training code

## 📚 BibTeX

💗 If you enjoy our Video-Skill-CoT and find some beneficial things, citing our paper would be the best support for us!

```
@article{lee2025videoskillcot,
  title={Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning},
  author={Lee, Daeun and Yoon, Jaehong and Cho, Jaemin and Bansal, Mohit},
  journal={arXiv preprint arXiv:2506.03525},
  year={2025}
}
```

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