Tensorflow code for CVPR 2017 paper: Learning a Deep Embedding Model for Zero-Shot Learning
Download data from here and unzip it unzip data.zip.
AwA_attribute.py will gives you ZSL performance on AwA with attribute.
AwA_wordvector.py will gives you ZSL performance on AwA with wordvector.
AwA_fusion.py will gives you ZSL performance on AwA with attribute and wordvector fusion.
CUB_attribute.pywill gives you ZSL performance on CUB with attribute.
ZSL and GZSL performance evaluated under GBU setting [1]: ResNet feature, GBU split, averaged per class accuracy.
AwA1_GBU.py will gives you ZSL and GZSL performance on AwA1 with attribute under GBU setting [1].
CUB1_GBU.py will gives you ZSL and GZSL performance on CUB1 with attribute under GBU setting [1].
| Model | T1 | u | s | H | T1 | u | s | H |
|---|---|---|---|---|---|---|---|---|
| DAP [2] | 44.1 | 0.0 | 88.7 | 0.0 | 40.0 | 1.7 | 67.9 | 3.3 |
| CONSE [3] | 45.6 | 0.4 | 88.6 | 0.8 | 34.3 | 1.6 | 72.2 | 3.1 |
| SSE [4] | 60.1 | 7.0 | 80.5 | 12.9 | 43.9 | 8.5 | 46.9 | 14.4 |
| DEVISE [5] | 54.2 | 13.4 | 68.7 | 22.4 | 52.0 | 23.8 | 53.0 | 32.8 |
| SJE [6] | 65.6 | 11.3 | 74.6 | 19.6 | 53.9 | 23.5 | 59.2 | 33.6 |
| LATEM [7] | 55.1 | 7.3 | 71.7 | 13.3 | 49.3 | 15.2 | 57.3 | 24.0 |
| ESZSL [8] | 58.2 | 6.6 | 75.6 | 12.1 | 53.9 | 12.6 | 63.8 | 21.0 |
| ALE [9] | 59.9 | 16.8 | 76.1 | 27.5 | 54.9 | 23.7 | 62.8 | 34.4 |
| SYNC [10] | 54.0 | 8.9 | 87.3 | 16.2 | 55.6 | 11.5 | 70.9 | 19.8 |
| SAE [11] | 53.0 | 1.8 | 77.1 | 3.5 | 33.3 | 7.8 | 54.0 | 13.6 |
| ** DEM (OURS)** |
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} } - [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.