Code repository for our paper entilted "Alignment-Free RGBT Salient Object Detection: Semantics-guided Asymmetric Correlation Network and A Unified Benchmark" accepted at TMM 2024.
arXiv version: https://arxiv.org/abs/2406.00917.
We are excited to announce that our new work "Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network" has been accepted to AAAI 2025! 🔗 GitHub Repository: PCNet
This is also part of our ongoing Alignment-Free RGB-T Salient Object Detection series. Stay tuned for updates regarding code and resources related to this new work. 🚀
If you think our work is helpful, please cite
@article{Wang2024alignment,
title={Alignment-Free RGBT Salient Object Detection: Semantics-guided Asymmetric Correlation Network and A Unified Benchmark},
author={Wang, Kunpeng and Lin, Danying and Li, Chenglong and Tu, Zhengzheng and Luo, Bin},
journal={IEEE Transactions on Multimedia},
year={2024}
}
We construct a novel benchmark dataset, containing 2000 unaligned visible-thermal image pairs directly captured from various real-word scenes, to facilitate research on alignment-free RGBT SOD.
The proposed dataset link can be found here. [baidu pan fetch code: irwv] or [google drive]
We analyze the proposed UVT2000 datset from several statistical aspects and also conduct a comparison between UVT2000 and other existing multi-modal SOD datasets.
RGB-T saliency maps can be found here. [baidu pan fetch code: xyu7] or [google drive]
RGB-D saliency maps can be found here. [baidu pan fetch code: jrjl] or [google drive]
RGB saliency maps can be found here. [baidu pan fetch code: kj6o] or [google drive]
ResNet50-based saliency maps can be found here. [baidu pan fetch code: xm4c]
ResNet50-based checkpoints can be found here. [baidu pan fetch code: svvk]
The pretrained parameters of our models can be found here. [baidu pan fetch code: ihri] or [google drive]
- Download the datasets for training and testing from here. [baidu pan fetch code: 075x]
- Download the pretrained parameters of the backbone from here. [baidu pan fetch code: mad3]
- Create directories for the experiment and parameter files.
- Please use
condato installtorch(1.12.0) andtorchvision(0.13.0). - Install other packages:
pip install -r requirements.txt. - Set your path of all datasets in
./Code/utils/options.py.
python -m torch.distributed.launch --nproc_per_node=2 --master_port=2212 train_parallel.py
python test_produce_maps.py
The implement of this project is based on the following link.
If you have any questions, please contact us ([email protected]).