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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}
}
``` |