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].
|------------|---------|-----------------------------|---------|-----------------------------| | | ZSL | GZSL | ZSL | GZSL |
| | AwA1 || CUB1 |||
| Model | T1 | u | s | H | T1 | u | s | H |
|---|---|---|---|---|---|---|---|---|
| DAP | 44.1 | 0.0 | 88.7 | 0.0 | 40.0 | 1.7 | 67.9 | 3.3 |
| CONSE | 45.6 | 0.4 | 88.6 | 0.8 | 34.3 | 1.6 | 72.2 | 3.1 |
| SSE | 60.1 | 7.0 | 80.5 | 12.9 | 43.9 | 8.5 | 46.9 | 14.4 |
| DEVISE | 54.2 | 13.4 | 68.7 | 22.4 | 52.0 | 23.8 | 53.0 | 32.8 |
| SJE | 65.6 | 11.3 | 74.6 | 19.6 | 53.9 | 23.5 | 59.2 | 33.6 |
| LATEM | 55.1 | 7.3 | 71.7 | 13.3 | 49.3 | 15.2 | 57.3 | 24.0 |
| ESZSL | 58.2 | 6.6 | 75.6 | 12.1 | 53.9 | 12.6 | 63.8 | 21.0 |
| ALE | 59.9 | 16.8 | 76.1 | 27.5 | 54.9 | 23.7 | 62.8 | 34.4 |
| SYNC | 54.0 | 8.9 | 87.3 | 16.2 | 55.6 | 11.5 | 70.9 | 19.8 |
| SAE | 53.0 | 1.8 | 77.1 | 3.5 | 33.3 | 7.8 | 54.0 | 13.6 |
| ** DEM (OURS)** |
| | Grouping ||
| First Header | Second Header | Third Header |
|---|---|---|
| Content | Long Cell | |
| Content | Cell | Cell |
New section | More | Data | And more | With an escaped '|' ||
[Prototype table]
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.