Awesome
DQnet: Cross-Model Detail Querying for Camouflaged Object Detection
This is the official implementaion of paper DQnet: Cross-Model Detail Querying for Camouflaged Object Detection.
Illustration
With the contextual representation of ViT as global cues, our model queries crucial local details from the multi-scale CNN features. The enhanced representations have clear boundaries as well as few background noise, corresponding well with underlying camouflaged objects.
Updates
- 2022-11-11
- Finished our paper.
Model
The model used pretrained weights for ResNet50 and ViT. They can be found at:
- Resnet50
- ViT
Usage
First clone the repository locally:
git clone https://github.com/CVPR23/DQnet.git
Second download the weights for ResNet50 and ViT.
Some core dependencies:
- timm == 0.4.12
- torch == 1.11.0
More details can be found in <./requirements.txt>
Datasets
More details can be found at:
- COD Datasets
- CAMO Datasets
- NC4K Datasets
- CHAMELEON Datasets
For training:
You can use our default configuration, like this:
$ python main.py --model-name=DQnet --config=configs/DQnet/DQnet.py --datasets-info ./configs/_base_/dataset/dataset_configs.json --info demo
You can also use :
$ sh train.sh
For testing:
You can use our default configuration, like this:
$ python test.py
You can also use :
$ sh test.sh
Paper Details
Method Detials
DQNet
RBQ
Comparison
Visualization of mutil-scale details querying
