LearningToCompare_ZSL

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Zero-Shot Learning part)

For Few-Shot Learning part, please visit here.

Requirements

Python 2.7

Pytorch 0.3

Data

Download data from here and unzip it unzip data.zip.

Run

ZSL and GZSL performance evaluated under GBU setting [1]: ResNet feature, GBU split, averaged per class accuracy.

AwA1_RN.py will give you ZSL and GZSL performance on AwA1 with attribute under GBU setting [1].

AwA2_RN.py will give you ZSL and GZSL performance on AwA2 with attribute under GBU setting [1].

CUB_RN.py will give you ZSL and GZSL performance on CUB with attribute under GBU setting [1].

Citing

If you use this code in your research, please use the following BibTeX entry.

@inproceedings{sung2018learning,
  title={Learning to Compare: Relation Network for Few-Shot Learning},
  author={Sung, Flood and Yang, Yongxin and Zhang, Li and Xiang, Tao and Torr, Philip HS and Hospedales, Timothy M},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2018}
}

References

• [1] Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly. Yongqin Xian, Christoph H. Lampert, Bernt Schiele, Zeynep Akata. arXiv, 2017.

• [2] Attribute-Based Classification forZero-Shot Visual Object Categorization. Christoph H. Lampert, Hannes Nickisch and Stefan Harmeling. PAMI, 2014.

• [3] Zero-Shot Learning by Convex Combination of Semantic Embeddings. Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S. Corrado, Jeffrey Dean. arXiv, 2013.

• [4] Zero-Shot Learning via Semantic Similarity Embedding. Ziming Zhang, Venkatesh Saligrama. ICCV, 2015.

• [5] DeViSE: A Deep Visual-Semantic Embedding Model. Andrea Frome*, Greg S. Corrado*, Jonathon Shlens*, Samy BengioJeffrey Dean, Marc’Aurelio Ranzato, Tomas Mikolov. NIPS, 2013.

• [6] Evaluation of Output Embeddings for Fine-Grained Image Classification. Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, Bernt Schiele. CVPR, 2015.

• [7] Latent Embeddings for Zero-shot Classification. Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, Bernt Schiele CVPR, 2016.

• [8] An embarrassingly simple approach to zero-shot learning. Bernardino Romera-Paredes, Philip H. S. Torr. ICML, 2015.

• [9] Label-Embedding for Image Classification. Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid. PAMI, 2016.

• [10] Synthesized Classifiers for Zero-Shot Learning. Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha. CVPR, 2016.

• [11] Semantic Autoencoder for Zero-Shot Learning. Elyor Kodirov, Tao Xiang, Shaogang Gong. CVPR, 2017.

• [12] Learning a Deep Embedding Model for Zero-Shot Learning. Li Zhang, Tao Xiang, Shaogang Gong. CVPR, 2017.