[ECCV 2024] Frequency-Spatial Entanglement Learning for Camouflaged Object Detection

Yanguang Sun, Chunyan Xu, Jian Yang, Hanyu Xuan, Lei Luo

Our work has been accepted for ECCV 2024. The code has already been open sourced.

If you are interested in our work, please do not hesitate to contact us at Sunyg@njust.edu.cn via email.

Prediction maps

We provide the prediction maps of our FSEL model in camouflaged object detection, salient object detection, and polyp segmentation tasks.

FSEL-camouflaged object detection (COD) (PVT/ResNet/Res2Net) [baidu,PIN:u5sb]

FSEL-salient object detection (SOD) (PVT/ResNet) [baidu,PIN:pelf]

FSEL-polyp segmentation (PS) (PVT/ResNet) [baidu,PIN:48bi]

Training weights

We give the training weights of our FSEL model in COD tasks.

Note that you should use the relevant network in the lib_initial file to test these .pth files

FSEL-COD-weights (PVT/ResNet/Res2Net) [baidu,PIN:u0mq]

Citation

If you use FSEL method in your research or wish to refer to the baseline results published in the Model, please use the following BibTeX entry.

@article{FSEL,
  title={Frequency-Spatial Entanglement Learning for Camouflaged Object Detection},
  author={Sun, Yanguang and Xu, Chunyan and Yang, Jian and Xuan, Hanyu and Luo, Lei},
  journal={arXiv preprint arXiv:2409.01686},
  year={2024}
}

@article{FSEL,
  title={Frequency-Spatial Entanglement Learning for Camouflaged Object Detection},
  author={Sun, Yanguang and Xu, Chunyan and Yang, Jian and Xuan, Hanyu and Luo, Lei},
  booktitle={European Conference on Computer Vision},
  year={2024},
  pages={343--360},
}