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

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].

Model AwA1 T1 u s H CUB 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) 68.4 32.8 84.7 47.3 51.7 19.6 57.9 29.2
Model AwA2 T1 u s H aPY T1 u s H
DAP [2] 46.1 0.0 84.7 0.0 33.8 4.8 78.3 9.0
CONSE [3] 44.5 0.5 90.6 1.0 26.9 0.0 91.2 0.0
SSE [4] 61.0 8.1 82.5 14.8 34.0 0.2 78.9 0.4
DEVISE [5] 59.7 17.1 74.7 27.8 39.8 4.9 76.9 9.2
SJE [6] 61.9 8.0 73.9 14.4 32.9 3.7 55.7 6.9
LATEM [7] 55.8 11.5 77.3 20.0 35.2 0.1 73.0 0.2
ESZSL [8] 58.6 5.9 77.8 11.0 38.3 2.4 70.1 4.6
ALE [9] 62.5 14.0 81.8 23.9 39.7 4.6 73.7 8.7
SYNC [10] 46.6 10.0 90.5 18.0 23.9 7.4 66.3 13.3
SAE [11] 54.1 1.1 82.2 2.2 8.3 0.4 80.9 0.9
DEM (OURS) 67.1 30.5 86.4 45.1   35.0 11.1 75.1 19.4
Model SUN T1 u s H
DAP [2] 39.9 4.2 25.1 7.2
CONSE [3] 38.8 6.8 39.9 11.6
SSE [4] 51.5 2.1 36.4 4.0
DEVISE [5] 56.5 16.9 27.4 20.9
SJE [6] 53.7 14.7 30.5 19.8
LATEM [7] 55.3 14.7 28.8 19.5
ESZSL [8] 54.5 11.0 27.9 15.8
ALE [9] 58.1 21.8 33.1 26.3
SYNC [10] 56.3 7.9 43.3 13.4
SAE [11]         40.3   8.8     18.0   11.8 
DEM (OURS) 61.9 20.5 34.3 25.6

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

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