Code and dataset repository for our AAAI 2025 paper: "Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network".

📄 arXiv: https://arxiv.org/pdf/2412.14576

The model and results are available now. [17th, Jul, 2025]

Thank you for your attention.

✨ Update (2026-01): Google Drive links added (recommended for international users).

• 📦 Dataset (UVT20K compressed): 👉 [Google Drive]
• 📌 Results & checkpoints (full mirror): 👉 [Google Drive]

The compressed UVT20K dataset contains annotations of saliency maps, edges, scribbles, and challenge attributes.
Download here:

• 📦 [Baidu Pan] (code: v2rc)
• 🌍 [Google Drive] (recommended)

✨ Google Drive mirror (recommended for international users):
All released results/checkpoints (same content as the Baidu Pan links below):
👉 [Google Drive]

• 📌 Predicted results (ours): [Baidu Pan] (code: eekm)
• 🧩 Model checkpoints: [Baidu Pan] (code: gvvw)
• 📊 Predicted results (compared methods): [Baidu Pan] (code: 6qqn)
• 🧩 Pretreined IHN: Baidu Pan(code: j2wp)

1. 📦 Download UVT20K for training and testing (see Dataset section above).
2. 🧠 Download the pretrained backbone parameters:
   • 📦 [Baidu Pan] (code: 3ifw)
3. 🧩 Download the pretrained parameters of IHN from: [IHN].
4. 📁 Organize dataset and pretrained model directories.
5. 🗂️ Create directories for experiments and checkpoints.
6. 🧪 Install PyTorch via conda: torch==1.12.0, torchvision==0.13.0.
7. 📦 Install other packages: pip install -r requirements.txt.
8. 🔧 Set dataset paths in ./options.py.

python -m torch.distributed.launch --nproc_per_node=2 --master_port=2212 train_parallel.py

python test_produce_maps.py

If you think our work is helpful, please cite:

@inproceedings{wang2025alignment,
  title={Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network},
  author={Wang, Kunpeng and Chen, Keke and Li, Chenglong and Tu, Zhengzheng and Luo, Bin},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={7},
  pages={7780--7788},
  year={2025}
}

This project is based on the following resources:

• Multi-modal homography estimator
• SOD Literature Tracking
• Matlab-based Evaluation

📮 For questions or feedback, feel free to email: kp.wang@foxmail.com