47 lines
2.3 KiB
Markdown
47 lines
2.3 KiB
Markdown
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---
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library_name: transformers
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license: mit
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pipeline_tag: image-text-to-text
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tags:
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- fine-grained-visual-recognition
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- chain-of-thought
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- vision-reasoning
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---
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# Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning
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This is the official model repository for the paper **[Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning](https://huggingface.co/papers/2602.07605)**.
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## Introduction
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**Fine-R1** is a Multi-modal Large Language Model (MLLM) specifically designed for **Fine-Grained Visual Recognition (FGVR)**. While general MLLMs often struggle with distinguishing between highly similar sub-categories, Fine-R1 bridges the gap between generative models and specialized discriminative models (like CLIP) through an R1-style training framework.
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### Key Innovations:
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- **Chain-of-Thought Supervised Fine-tuning (CoT-SFT)**: The model is trained on high-quality FGVR CoT datasets, teaching it to perform visual analysis, consider candidate sub-categories, and compare them before predicting.
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- **Triplet Augmented Policy Optimization (TAPO)**: This includes Intra-class Augmentation to handle visual variance and Inter-class Augmentation to maximize distinction between similar sub-categories.
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With only 4-shot training, Fine-R1 excels in identifying both seen and unseen sub-categories, outperforming many general reasoning MLLMs and contrastive models.
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## Resources
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- **Paper:** [Hugging Face Papers](https://huggingface.co/papers/2602.07605)
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- **GitHub:** [PKU-ICST-MIPL/FineR1_ICLR2026](https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026)
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## Usage
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This model is compatible with the Hugging Face `transformers` library. For detailed instructions on environment setup, training scripts, and evaluation pipelines (closed-world and open-world), please refer to the official [GitHub Repository](https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026).
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## Citation
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If you find Fine-R1 helpful in your research, please cite the following paper:
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```bibtex
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@article{he2026finer1,
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title={Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning},
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author={He, Hulingxiao and Geng, Zijun and Peng, Yuxin},
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journal={arXiv preprint arXiv:2602.07605},
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year={2026}
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}
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```
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## License
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This project is licensed under the MIT License.
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