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LFSOD-CDINet

This project provides the code and results for 'Light Field Salient Object Detection with Sparse Views via Complementary and Discriminative Interaction Network', IEEE TCSVT, 2023. paper link

Network architecture

<div align=center> <img src="https://github.com/GilbertRC/LFSOD-CDINet/blob/main/Figs/Network.png"> </div>

Requirements

python 3.7 + TensorFlow 1.14.0

Note: We provide a modified layer.py (code: 7d8i) for TensorFlow 1.14.0. The added layer_norm_initialized() enables initializing Layer_Norm with pre-trained parameters. You can put it under 'your_Anaconda_envs/Lib/site-packages/tensorflow/contrib/layers/python/layers/' folder.

Training

  1. Download the TrainingSet (code: t7gt) and put it under './dataset/' folder.
  2. Download the pre-trained vgg-16 model (code: kq1o) and mpi model (code: c3tj) and put them under './models/' folder.
  3. Run train.py (default to the HFUT-Lytro Illum dataset).

Test using pre-trained model

  1. Download the TestSet (code: hdl2) and put it under './dataset/' folder.
  2. Download our pre-trained model_HFUT (code: k28i) and model_DUTLF-V2 (code: h8ou) and put them under './checkpoints/' folder.
  3. Run test.py. The SOD results will be saved under './results/' folder.

Note: In the paper, we use model_HFUT to test the HFUT-Lytro Illum & HFUT-Lytro datasets and use model_DUTLF-V2 to test the DUTLF-V2 dataset.

Saliency maps and performance

We provide results (code: lau2) of our CDINet on 3 datasets (HFUT-Lytro Illum, HFUT-Lytro and DUTLF-V2)

<div align=center> <img src="https://github.com/GilbertRC/LFSOD-CDINet/blob/main/Figs/Table.png"> </div>

Citation

@ARTICLE{LFSOD-CDINet,  
  title={Light Field Salient Object Detection with Sparse Views via Complementary and Discriminative Interaction Network},
  author={Yilei Chen and Gongyang Li and Ping An and Zhi Liu and Xinpeng Huang and Qiang Wu},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2024},
  volume={34},
  number={2},
  pages={1070-1085},
  month={Feb.}}

Any questions regarding this work can contact yileichen@shu.edu.cn.