Codes for the CVPR 2021 paper: Contrastive Embedding for Generalized Zero-Shot Learning [CVPR2021][arxiv].
Keras implementation [code]. Much appreciation for @webcsm's efforts!
This codes requires the following:
- Python 3.6
- Pytorch 1.2.0
- scikit-learn
Download the dataset (AWA1/AWA2/CUB/FLO/SUN) from the work of Xian et al. (CVPR2017), and save correspongding data into directory ./data/. Here, we provide the semantic descriptor for CUB, which is the 1,024-dimensional class embeddings generated from textual descriptions sent_splits.mat.
Run python CE_GZSL.py with the following args:
-
--dataset: datasets, e.g: CUB. -
--class_embedding: the semantic descriptors to use, e.g: sent or att. -
--syn_num: number synthetic features for each class. -
--batch_size: the number of the instances in a mini-batch. -
--attSize: size of semantic features. -
--nz: size of the Gaussian noise. -
--embedSize: size of embedding h. -
--outzSize: size of non-liner projection z. -
--nhF: size of the hidden units comparator network F. -
--ins_weight: weight of the classification loss when learning G. -
--cls_weight: weight of the score function when learning G. -
--ins_temp: temperature in instance-level supervision. -
--cls_temp: temperature in class-level supervision -
--manualSeed: manual seed. -
--nclass_all: number of all classes. -
--nclass_seen: number of seen classes
For example:
python3.6 CE_GZSL.py --dataset CUB --class_embedding sent --syn_num 100 --batch_size 2048 --attSize 1024 --nz 1024 --embedSize 2048 --outzSize 512 --nhF 2048 --ins_weight 0.001 --cls_weight 0.001 --ins_temp 0.1 --cls_temp 0.1 --manualSeed 3483 --nclass_all 200 --nclass_seen 150If you find this useful, please cite:
@InProceedings{Han_2021_CVPR,
author = {Han, Zongyan and Fu, Zhenyong and Chen, Shuo and Yang, Jian},
title = {Contrastive Embedding for Generalized Zero-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {2371-2381}
}