SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction
Paper โข 2605.20110 โข Published
How to use rookiexiong/SetCon-8B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-segmentation", model="rookiexiong/SetCon-8B", trust_remote_code=True) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rookiexiong/SetCon-8B", trust_remote_code=True, dtype="auto")SetCon-8B is the model checkpoint for SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction.
Please use this checkpoint together with the official codebase:
git clone https://github.com/rookiexiong7/SetCon.git
cd SetCon
uv sync --extra latest
source .venv/bin/activate
Single-image inference:
python demo.py \
--image-path assets/room.jpg \
--query-text "the target objects" \
--model-path path/to/SetCon-8B
This model is intended for research on open-ended referring image/video segmentation.
The model may produce incomplete or inaccurate masks for ambiguous expressions, small objects, crowded scenes, or out-of-domain visual concepts.
If you find our work helpful for your research, please consider giving a star โญ and citation ๐
@article{zhang2026setcon,
title={SetCon: towards open-ended referring segmentation via set-level concept prediction},
author={Zhixiong Zhang and Yizhuo Li and Shuangrui Ding and Yuhang Zang and Shengyuan Ding and Long Xing and Yibin Wang and Qiaosheng Zhang and Jiaqi Wang},
journal={arXiv preprint arXiv:2605.20110},
year={2026}
}