Conditional Attribute Network CANet

This is the pytorch code for the paper:

Title: Learning Conditional Attributes for Compositional Zero-Shot Learning
Authors: Qingsheng Wang, Lingqiao Liu, Chenchen Jing, et.al.
Publication: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2023
Published Paper: https://arxiv.org/abs/2305.17940

If you find this work interesting please cite

@inproceedings{wang2023learning,
  title={Learning Conditional Attributes for Compositional Zero-Shot Learning},
  author={Qingsheng Wang, Lingqiao Liu, Chenchen Jing, Hao Chen, Guoqiang Liang, Peng Wang, Chunhua Shen},
  booktitle={CVPR},
  year={2023}
}

All code was implemented using Python 3.10 and Pytorch 1.12 on Ubuntu.

1. Data Preparation

UT-Zappos50K: http://vision.cs.utexas.edu/projects/finegrained/utzap50k/ut-zap50k-images.zip

MIT-States: http://wednesday.csail.mit.edu/joseph_result/state_and_transformation/release_dataset.zip

C-GQA: https://s3.mlcloud.uni-tuebingen.de/czsl/cgqa-updated.zip

1. Download datasets UT-Zappos50K, MIT-States, and C-GQA and unzip them into a dataset folder, e.g., /home/XXX/datasets. Rename the dataset folder names as ut-zap50k, mit-states, and cgqa.

2. Download data splits for UT-Zappos50K and MIT-States at https://www.senthilpurushwalkam.com/publication/compositional/compositional_split_natural.tar.gz

3. Unzip the downloaded file compositional_split_natural.tar.gz and place the sub-folders mit-states and ut-zap50k into the corresponding dataset folder. Note that the cgqa dataset zip file contains its own data split.

4. Now, we have the following folder structure for the three datasets:

   > /home/XXX
     > datasets
       > ut-zap50k # can also be mit-states or cgqa
       - metadata_compositional-split-natural.t7
       > compositional-split-natural
           - test_pairs.txt
           - train_pairs.txt
           - val_pairs.txt
       # ===Create this empty folder manually for UT-Zappos50K===# 
       > images
       # ======Only UT-Zappos50K has the following folders=======#
       > Boots
       > Sandals
       > Shoes
       > Slippers

5. Run /utils/reorganize_utzap.py to reorganize images in UT-Zappos50K, where set DATA_FOLDER='/home/XXX/datasets' in line 20.

6. (Optional) Delete sub-folders Boots, Sandals, Shoes, and Slippers in the folder ut-zap50k if you want to save some disk space.

2. Inference

We provide the trained parameters for all three datasets:

Google Drive: https://drive.google.com/drive/folders/1IGXPMRossFuVxIeWzvKRXrczXDNhHG1F?usp=sharing

Baidu Netdisk: https://pan.baidu.com/s/1D3BaNKgTjy7dxI8fvcbbDA?pwd=2ity

1. Download all trained parameters into the manually created folder saved model. Now we have the folder structure:

   > CANet-CZSL-master
     > ...
     > model
     > saved model
       - saved_cgqa.t7
       - saved_mit-states.t7
       - saved_ut-zap50k.t7
     > utils
     > ...
   

2. Open test.py, you have to specify some arguments before running this code: args.dataset, args.data_root, and device in lines 18-21.

3. Run this code. If you run the code for the first time, you will get a file resnet18_feature_vectors.t7 in each dataset folder. This file contains visual embeddings of all images extracted by resnet-18 without any image augmentation. In this way, all visual embeddings are retrieved by image filename rather than being computed every time and thus reducing inference time significantly. You will get exactly the same results reported in the paper if everything sets (Results in the paper are rounded).

   UT-Zappos50K:

   

   MIT-States:

   

   C-GQA:

   

Warning: If you were using an older version pytorch, you would get slightly different results because the pre-trained parameters of the image backbone are different from the lasted version pytorch.

3. Training

You can train the model from scratch.

1. You can edit configurations for different runs in flags.py and configs/dataset name CANet.yml. The file flags.py defines some of the shared hyper-parameters and the *.yml files define dataset-specific hyper-parameters.

2. Open model/word_embedding.py and set the global variable DATA_FOLDER = '/home/XXX/word_embedding/' in line 4 to tell where you save these pre-trained word embeddings.

3. Open train.py and set args.dataset, args.data_root, and device in lines 34-37 before your training.

You will notice that a folder named logs is created automatically after starting the training. This folder contains the saved checkpoint model, best model, tensorboard file, and all history results in a csv file.

Acknowledgement

This code package is based on the CZSL library at https://github.com/ExplainableML/czsl. We show our deep appreciation for their contribution.