Code release for the paper ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification (TCSVT 2020).
- UIUC-Sports contains 1578 sports scene images of 8 classes: bocce (137), polo (182),rowing (250), sailing (190), snowboarding (190), rock climb-ing (194), croquet (236) and badminton (200). A training set of 749 images and a test set of 749 images are randomly sampled from the entire dataset.
- You can download the dataset at http://vision.stanford.edu/lijiali/event_dataset/. (Li-Jia Li and Li Fei-Fei. What, where and who? Classifying event by scene and object recognition . IEEE Intern. Conf. in Computer Vision (ICCV). 2007 (PDF) )
- The style of our randomly divided dataset is shown in the related Excel table. You can divide the dataset according to the name of the sample in our table.
- python=2.7
- PyTorch=1.4.0
- torchvision=0.5.0
- pillow=6.2.1
- numpy=1.15.4
- Download datasets
- You can run the code using the following command:
python UIUC_ReMarNet.py
python UIUC_Baseline.py
Dataset | Measure | Baseline | Ours |
UIUC-Sports | Mean | 0.9476 | 0.9581 |
Std. | 0.0045 | 0.0038 |
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