This is a PyTorch implementation for [One-Shot Learning for Long-Tail Visual Relation Detection] This is an improved version of the code.
Because of the new coronavirus, we can't go back to school, so our open source work was delayed. We will upload code as soon as possible. If you have any questions, please contact the first author.
- code optimization
- end to end
VRD-one
| PredCls 5-way 1-shot | PredCls 10-way 1-shot | SGCls 5-way 1-shot | SGCls 10-way 1-shot | |
|---|---|---|---|---|
| Ours(old | 48.4% | 33.5% | 22.3% | 20.9% |
| Ours | 49.9% | 35.9% | 25.2% | 19.5% |
VG-one
| PredCls 5-way 1-shot | PredCls 10-way 1-shot | SGCls 5-way 1-shot | SGCls 10-way 1-shot | |
|---|---|---|---|---|
| Ours(old | 56.3% | 37.5% | 14.9% | 13.2% |
| Ours | 56.3% | 40.7% | 15.2% | 14.3% |
- Python 3
- Python packages
- pytorch 1.0
- cython
- matplotlib
- numpy
- scipy
- opencv
- pyyaml
- packaging
- tensorboardX
- tqdm
- pillow
- scikit-image
- An NVIDIA GPU and CUDA 8.0 or higher. Some operations only have gpu implementation.
Download it here. Unzip it under the data folder. You should see a vg-one folder unzipped there. It contains .json annotations that suit the dataloader used in this repo.
Download it here. Unzip it under the data folder. You should see a vrd-one folder unzipped there. It contains .json annotations that suit the dataloader used in this repo.
Our model is based on Faster RCNN, you need to use Faster RCNN model to extract image features, and put them in the $oneshot/data.
python main.py
If you use this code in your research, please use the following BibTeX entry.
If you have any questions, please contact us ( wangweitao@seu.edu.cn ).