---
datasets:
- lmms-lab/RefCOCO
license: apache-2.0
pipeline_tag: image-segmentation
tags:
- multimodal
- referring-image-segmentation
---

# TALENT: Target-aware Efficient Tuning for Referring Image Segmentation

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.

## Resources

- **Paper:** [TALENT: Target-aware Efficient Tuning for Referring Image Segmentation](https://huggingface.co/papers/2604.00609)
- **Repository:** [GitHub - Kimsure/TALENT](https://github.com/Kimsure/TALENT)

## Usage

To evaluate the model, follow the installation instructions in the [GitHub repository](https://github.com/Kimsure/TALENT) and run the following script:

```bash
bash run_scripts/test.sh
```

To visualize the results, you can set the `visualize` flag to `True` in the configuration file.

## Acknowledgements

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.

## Citation

If you find this work useful, please cite:

```bibtex
@article{talent2026,
  title={TALENT: Target-aware Efficient Tuning for Referring Image Segmentation},
  author={Shuo Jin, Siyue Yu, Bingfeng Zhang, Chao Yao, Meiqin Liu, Jimin Xiao},
  journal={arXiv preprint arXiv:2604.00609},
  year={2026}
}
```