DeepEmbeddingModel_ZSL

Tensorflow code for CVPR 2017 paper: Learning a Deep Embedding Model for Zero-Shot Learning

Li Zhang

Requirement

Python 2.7

Tensorflow > 1.0

Data

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

Run

AwA_attribute.py will give you ZSL performance on AwA with attribute.

AwA_wordvector.py will give you ZSL performance on AwA with wordvector.

AwA_fusion.py will give you ZSL performance on AwA with attribute and wordvector fusion.

CUB_attribute.pywill give you ZSL performance on CUB with attribute.

GBU setting

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

AwA1_GBU.py will give you ZSL and GZSL performance on AwA1 with attribute under GBU setting [1].

AwA2_GBU.py will give you ZSL and GZSL performance on AwA2 with attribute under GBU setting [1].

CUB1_GBU.py will give you ZSL and GZSL performance on CUB with attribute under GBU setting [1].

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

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

PyTorch implementation

DeepEmbeddingModel_ZSL-Pytorch

Citing

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

@inproceedings{zhang2017learning,
  title={Learning a deep embedding model for zero-shot learning},
  author={Zhang, Li and Xiang, Tao and Gong, Shaogang},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2017}
}

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.