This repository hosts the code of the research presented in "CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection". We provide both PyTorch and MindSpore versions of the code.

• Python 3.8
• PyTorch 2.1
• torch-npu 2.1.0
• torchvision

To set up the necessary environment, follow these steps:

pip install torch==1.9 torchvision

To train the model, run the following command:

python train.py

Configuration settings can be adjusted in options.py. This file contains various parameters and settings that you can modify to customize the training process.

For testing the model, use the following command:

python test.py

Similar to training, the testing configurations can be found and altered in options.py.

This repository is organized into two primary directories to accommodate both PyTorch and MindSpore codebases, ensuring compatibility and ease of use across different deep learning frameworks. Each directory mirrors the following structure:

• options.py: Configuration file where all the settings for training and testing are modified.
• ResNet.py: Contains ResNet-related backbone models and configurations.
• test.py: The script used for testing the models.
• train.py: The script used for training the models.
• pytorch_iou: Contains code for IoU (Intersection over Union) loss computation.
• data.py: Includes operations related to data loading and processing.
• clip: Files related to the CLIP model.
• Net.py: The main network architecture.

@inproceedings{eccv2024cola,
  title={CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection},
  author={Hao, Shuang and Zhong, Chunlin and Tang, He},
  booktitle={European Conference on Computer Vision,
  year={2024}
}