Awesome

Semantics Disentangling for Generalized Zero-Shot Learning

This is the official implementation for paper

Zhi Chen, Yadan Luo, Ruihong Qiu, Zi Huang, Jingjing Li, Zheng Zhang.
Semantics Disentangling for Generalized Zero-Shot Learning
International Conference on Computer Vision (ICCV) 2021.

Semantics Disentangling for Generalized Zero-shot Learning

Supplementary Experimental Results

In the paper, we followed the datasets provided in [15], in which the visual features in FLO dataset are normalized, and the semantic description of CUB dataset is the CNN-RNN sentence embeddings (1024D). We hereby provide extra comparison results on the visual features provided by GBU setting [17].

TODO

• <del> Results on CUB with attributes </del>
• <del> Results on FLO without normalization </del>
• <del> Results on SUN </del>
• <del> Release the code of supplementary experiments </del>

Requirements

The implementation runs on

• Python 3.6

• torch 1.3.1

• Numpy

• Sklearn

• Scipy

Usage

Put your datasets in SDGZSL_data folder and run the scripts in the folder.

The extracted features for APY and AWA datasets are from [1], FLO and CUB datasets are from [2]. For the fine-tuned features, AWA,FLO and CUB are from [3]. The APY fine-tuned features are extracted from us.

[1] Xian, Yongqin, et al. "Feature generating networks for zero-shot learning." CVPR 2018.

[2] Yu, Yunlong, et al. "Episode-based prototype generating network for zero-shot learning." CVPR 2020.

[3] Narayan, Sanath, et al. "Latent embedding feedback and discriminative features for zero-shot classification." ECCV 2020.

[4] Y. Liu, et al. "Attribute attention for semantic disambiguation in zero-shot learning." CVPR 2019.

[7] S. Min, et al. "Domain-aware visual bias eliminating for generalized zeroshot learning." CVPR 2020.

[8] Z. Chen, et al, "CANZSL: Cycleconsistent adversarial networks for zero-shot learning from natural language," WACV, 2020.

[9] J. Li, et al. "Leveraging the invariant side of generative zero-shot learning." CVPR, 2019.

[10] E. Schonfeld, et al. "Generalized zero-and few-shot learning via aligned variational autoencoders." CVPR, 2019.

[11] Y. Xian, et al. "f-vaegan-d2:A feature generating framework for any-shot learning." CVPR, 2019.

[12] B. Tong, et al. "Hierarchical disentanglement of discriminative latent features for zero-shot learning." CVPR, 2019.

[13] S. Narayan, et al. "Latent embedding feedback and discriminative features for zero-shot classification." ECCV, 2020.

[15] Y. Yu, et al. "Episode-based prototype generating network for zero-shot learning." CVPR, 2020.

[16] C., Yu-Ying, et al. "Adaptive and generative zero-shot learning." ICLR, 2020.

[17 ] Y. Xian, et al. "Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly." TPAMI, 2018.

Citation:

If you find this useful, please cite our work as follows:

@inproceedings{chen2021semantics,
	title={Semantics Disentangling for Generalized Zero-shot Learning},
	author={Chen, Zhi and Luo, Yadan and Qiu, Ruihong and Huang, Zi and Li, Jingjing and Zhang, Zheng},
	booktitle={ICCV},
	year={2021}
}