Add model card and pipeline tag
Browse filesThis PR improves the model card for the TALENT model. Key changes include:
- Added the `image-segmentation` pipeline tag to the metadata for better discoverability.
- Included links to the research paper and the official GitHub repository.
- Added a brief introduction and usage instructions based on the GitHub documentation.
- Formatted the metadata with relevant tags.
README.md
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---
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license: apache-2.0
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datasets:
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- lmms-lab/RefCOCO
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tags:
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- multimodal
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---
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datasets:
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- lmms-lab/RefCOCO
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license: apache-2.0
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pipeline_tag: image-segmentation
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tags:
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- multimodal
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- referring-image-segmentation
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---
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# TALENT: Target-aware Efficient Tuning for Referring Image Segmentation
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TALENT is a framework for Referring Image Segmentation (RIS) designed to address the "non-target activation" (NTA) issue in parameter-efficient tuning. It introduces a Rectified Cost Aggregator (RCA) to aggregate text-referred features and a Target-aware Learning Mechanism (TLM) to calibrate activation into accurate target localization.
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## Resources
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- **Paper:** [TALENT: Target-aware Efficient Tuning for Referring Image Segmentation](https://huggingface.co/papers/2604.00609)
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- **Repository:** [GitHub - Kimsure/TALENT](https://github.com/Kimsure/TALENT)
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## Usage
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To evaluate the model, follow the installation instructions in the [GitHub repository](https://github.com/Kimsure/TALENT) and run the following script:
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```bash
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bash run_scripts/test.sh
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```
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To visualize the results, you can set the `visualize` flag to `True` in the configuration file.
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## Acknowledgements
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The code for TALENT is based on [CRIS](https://github.com/DerrickWang005/CRIS.pytorch), [ETRIS](https://github.com/kkakkkka/ETRIS), and previous TALENT implementations. We thank the authors for their open-sourced code.
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## Citation
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If you find this work useful, please cite:
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```bibtex
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@article{talent2026,
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title={TALENT: Target-aware Efficient Tuning for Referring Image Segmentation},
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author={...},
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journal={arXiv preprint arXiv:2604.00609},
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year={2026}
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}
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```
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