Official Pytorch implementation for the paper titled "CAFENet: Class-Agnostic Few-Shot Edge Detection Network" presented on BMVC 2021.
| Network architecture overview of proposed CAFENet | Qualitative Results on the SBD-5i Dataset. |
|---|---|
 |
 |
We tackle a novel few-shot learning challenge, few-shot semantic edge detection, aiming to localize boundaries of novel categories using only a few labeled samples. Reliable boundary information has been shown to boost the performance of semantic segmentation and localization, while also playing a key role in its own right in object reconstruction, image generation and medical imaging. However, existing semantic edge detection techniques require a large amount of labeled data to train a model. To overcome this limitation, we present Class-Agnostic Few-shot Edge detection Network (CAFENet) based on a meta-learning strategy. CAFENet employs a semantic segmentation module in small-scale to compensate for the lack of semantic information in edge labels. To effectively fuse the semantic information and low-level cues, CAFENet also utilizes an attention module which dynamically generates multi-scale attention map, as well as a novel regularization method that splits high-dimensional features into several low-dimensional features and conducts multiple metric learning. Since there are no existing datasets for few-shot semantic edge detection, we construct two new datasets, FSE-1000 and SBD-5i, and evaluate the performance of the proposed CAFENet on them. Extensive simulation results confirm that CAFENet achieves better performance compared to the baseline methods using fine-tuning or few-shot segmentation.
sys.platform: linux
Python: 3.7.7
Pytorch : 1.5.0
TorchVision: 0.6.1
Cudatoolkit : 10.1.243
scikit-learn : 0.23.2
numpy : 1.19.0
FSE-1000 : download here.
SBD-5i : download here.
#For FSE-1000
python FSE-1000.train.py
#For SSD
python SSD_train.py --gpu-ids {GPU device number}
--work_dir {dir to save logs and models}
--config {train config file path}