(ECCV 2024) Open-Vocabulary Camouflaged Object Segmentation

@inproceedings{OVCOS_ECCV2024,
  title={Open-Vocabulary Camouflaged Object Segmentation},
  author={Pang, Youwei and Zhao, Xiaoqi and Zuo, Jiaming and Zhang, Lihe and Lu, Huchuan},
  booktitle=ECCV,
  year={2024},
}

Note

CAD dataset can be found at https://drive.google.com/file/d/1XhrC6NSekGOAAM7osLne3p46pj1tLFdI/view?usp=sharing

Details of the proposed OVCamo dataset can be found in the document for our dataset.

Prepare Dataset

Note

CAD subset can be found in

1. Prepare the training and testing splits: See the document in our dataset for details.
2. Set the training and testing splits in the yaml file env/splitted_ovcamo.yaml:
   • OVCamo_TR_IMAGE_DIR: Image directory of the training set.
   • OVCamo_TR_MASK_DIR: Mask directory of the training set.
   • OVCamo_TR_DEPTH_DIR: Depth map directory of the training set. Depth maps of the training set which are generated by us, can be downloaded from https://github.com/lartpang/OVCamo/releases/download/dataset-v1.0/depth-train-ovcoser.zip
   • OVCamo_TE_IMAGE_DIR: Image directory of the testing set.
   • OVCamo_TE_MASK_DIR: Mask directory of the testing set.
   • OVCamo_CLASS_JSON_PATH: Path of the json file class_info.json storing class information of the proposed OVCamo.
   • OVCamo_SAMPLE_JSON_PATH: Path of the json file sample_info.json storing sample information of the proposed OVCamo.

Training/Inference

1. Install dependencies: pip install -r requirements.txt.
   1. The versions of torch and torchvision are listed in the comment of requirements.txt.
2. Run the script to:
   1. train the model: python .\main.py --config .\configs\ovcoser.py --model-name OVCoser;
   2. inference the model: python .\main.py --config .\configs\ovcoser.py --model-name OVCoser --evaluate --load-from <path of the local .pth file.>.

Evaluate the Pretrained Model

1. Download the pretrained model.
2. Run the script: python .\main.py --config .\configs\ovcoser.py --model-name OVCoser --evaluate --load-from model.pth.

Evaluate Our Results

1. Download our results and unzip it into <path>/ovcoser-ovcamo-te.
2. Run the script: python .\evaluate.py --pre <path>/ovcoser-ovcamo-te

LICENSE

• Code: MIT LICENSE
• Dataset:

  OVCamo by Youwei Pang, Xiaoqi Zhao, Jiaming Zuo, Lihe Zhang, Huchuan Lu is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International