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README.md

ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification

Code release for the paper ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification (TCSVT 2020).

Dataset

UIUC-Sports

  • 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.

Requirements

  • python=2.7
  • PyTorch=1.4.0
  • torchvision=0.5.0
  • pillow=6.2.1
  • numpy=1.15.4

Training

  • Download datasets
  • You can run the code using the following command:
python UIUC_ReMarNet.py
python UIUC_Baseline.py 

Results

Dataset Measure Baseline Ours
UIUC-Sports Mean 0.9476 0.9581
Std. 0.0045 0.0038
## Citation

If you find this paper useful in your research, please consider citing:


Contact

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Code release for the paper ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification (TCSVT 2020)

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