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].

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 [12]	68.4	32.8	84.7	47.3	51.7	19.6	57.9	29.2
RN (OURS)	68.2	31.4	91.3	46.7	55.6	38.1	61.4	47.0

Model	AwA2 T1	u	s	H
DAP [2]	46.1	0.0	84.7	0.0
CONSE [3]	44.5	0.5	90.6	1.0
SSE [4]	61.0	8.1	82.5	14.8
DEVISE [5]	59.7	17.1	74.7	27.8
SJE [6]	61.9	8.0	73.9	14.4
LATEM [7]	55.8	11.5	77.3	20.0
ESZSL [8]	58.6	5.9	77.8	11.0
ALE [9]	62.5	14.0	81.8	23.9
SYNC [10]	46.6	10.0	90.5	18.0
SAE [11]	54.1	1.1	82.2	2.2
DEM [12]	67.1	30.5	86.4	45.1
RN (OURS)	64.2	30.0	93.4	45.3

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.

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