🔥 TGG

The source code of our ACM MM 2019 conference paper "TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning".

Requirements

• python 3.7.1+
• pytorch 1.0.0+
• numpy 1.15.4+
• scipy 1.2.0+
• scikit-learn 0.21.2+
• requests 2.21.0+
• matplotlib 3.0.2+
• CUDA 10.0+
• cudnn 6.0.21+

Datasets

• aPY. Attribute Pascal and Yahoo (aPY) is a small-scale coarse-grained dataset with 64 attributes.
• CUB Caltech-UCSDBirds 200-2011 (CUB) is a fine-grained and medium scale dataset with respect to both number of images and number of classes, i.e. 11, 788 images from 200 different types of birds annotated with 312 attributes.
• AwA1 Animals with Attributes (AWA1) is a coarse-grained dataset that is medium-scale in terms of the number of images, i.e. 30, 475 and small-scale in terms of number of classes, i.e. 50 classes.
• AwA2 Animals with Attributes2 (AWA2) is introduced by [9], which contains 37, 322 images for the 50 classes of AWA1 dataset from public web sources, i.e. Flickr, Wikipedia, etc., making sure that all images of AWA2 have free-use and redistribution licenses and they do not overlap with images of the original Animal with Attributes dataset.
• SUN SUN is a fine-grained and medium-scale dataset with respect to both number of images and number of classes, i.e. SUN contains 14340 images coming from 717 types of scenes annotated with 102 attributes.

NOTE: our TGG algorithm is feature-agnostic, hence you can use any type of visual feature as input (In our implement, following [1], ResNet101 feature is used for a fair comparison).

Usage

Download the datasets from here and put them into the tgg-pytorch/data/, then

1. Build the class-level graphs with ConceptNet5.5

$ cd tgg-pytorch/preprocess/
$ python graph_construction.py

The class-level graph (pickle file) will be saved at tgg-pytorch/data/preprocessed_data/${dataset_name}. In this repo, we use two small datasets (i.e., aPY and AwA) for two fast examples, and the preprocessed class-level files are available at:

tgg-pytorch/data/preprocessed_data/apy/apy_class_adj_byConceptNet5.5_.pkl
tgg-pytorch/data/preprocessed_data/awa/awa_class_adj_byConceptNet5.5_.pkl

and their visualization is shown below:

2. Train GAN models with the code in tgg-pytorch/AUFS_ZSL

Here AUFS_ZSL is our another work that is published in IJCAI 2018. See https://github.com/zcrwind/AUFS_ZSL for more details. For convenience, similarly, we provide two pre-trained AUFS models at:

tgg-pytorch/gan_checkpoints/apy/checkpoint_apy_iter951_accUnseen29.80_accSeen64.53.pkl
tgg-pytorch/gan_checkpoints/awa/checkpoint_awa_iter8401accUnseen57.34_accSeen72.49.pkl

3. Train and evaluate our TGG model:

for aPY dataset example:

$ sh main_aPY.sh

for AwA dataset example:

$ sh main_AwA1.sh

NOTE: modify hyperparameters in config files as needed, where the suitable learning rate is of great importance.

Experimental Results

Due to space constraints, we refer the readers to our paper for the results of conventional ZSL, GZSL and FSL.

References

[1] Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. 2018. Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1199–1208.

If you make use of this code in your work, please cite the paper:

@inproceedings{zhang2019tgg,
        title={TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning},
        author={Zhang, Chenrui and Lyu, Xiaoqing and Tang, Zhi},
        booktitle={ACM Conference on Multimedia},
        pages={1641--1649},
        year={2019}
}