Awesome

UODTN

Code release for "Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization" (CVPR 2019)

We implement our experiments with python 3.6, numpy 1.15 and PyTorch 1.0.

Step 1 Generate graph for AwA

cd GCN/materials/AWA2 

Download: http://nlp.stanford.edu/data/glove.6B.zip
Unzip it, find and put glove.6B.300d.txt here.

Construct graph:

python make_graph_animal_step1.py  
python make_graph_animal_step2.py  

Result in animals_graph_all.json, the graph for AwA2 which contains 255 nodes.

Extract clasifiers from Resnet-50 pretrained on ImageNet for categories that shared by the construted graph and ImageNet-1K.

Download: https://download.pytorch.org/models/resnet50-19c8e357.pth
Rename and put it as materials/resnet50-raw.pth

python extract_127+24_from_1K.py  

Result in 151_cls_from_1K.

Step 2 Prepare data

We upload the source domain of I2AwA :
https://drive.google.com/file/d/1GdDZ1SvEqGin_zeCAGaJn0821vC_PJmc/view?usp=sharing
Or
https://pan.baidu.com/s/122-cvnjhYb0mB1zf4nShdA with password: ibee

For the target domain, one can download from http://cvml.ist.ac.at/AwA2/. The link is as follow:

http://cvml.ist.ac.at/AwA2/AwA2-data.zip

NOTE THAT WE DO NOT HAVE THE RIGHTS FOR THE SHARED IMAGES. PLEASE DO NOT USE OUR DATASET FOR COMMERCIAL PURPOSE.

We recommend that I2AwA can be used for traditional domain adaptation.

Step 3 Train classifier on source domain

We find that directly finetune classifiers on source domain and then transfer 40 known classifiers to unknown categories via GCN resulting very poor results. The reason may be in that using only 40 known classifiers as supervision information to train GCN is insufficient. Therefore, we utilize 127+24 classifiers pretrained on ImageNet-1K to train GCN. Specifically, 24 indicates that the classifiers are in source domain while the other 127 classifiers are not in target domain but are included in the constructed graph for target domain. We need to train the missing 16 classifiers for source domain. However, the classifiers pretrained on ImageNet-1K is discrepant from source domain. Thus, we fix the 24 classifiers and train 16 classifiers on source domain. Then we use these 127+24+16 classifiers to train GCN and obtain the initail classifiers for unknown categories.

cd UODTN  
python train_40_cls_with_help_of_1K.py  

Result in 151+16_cls_from_1K_ft and base_net_pretrained_on_I2AwA2_source_only. 151+16_cls_from_1K_ft includes the original 127+24 classifiers and the additional 16 classifiers finetuned from the source domain. "base_net_pretrained_on_I2AwA2_source_only" is a trained feature extractor for I2AwA.

Step 4 Train UODTN

Train a GCN:

cd GCN  
python train_gcn_basic_awa_ezhuo_2019.py  
python extract_50_cls_from_all_graph_for_awa.py  

Result in "awa_50_cls_basic", which contains 50 initial classifiers for AwA2.

Prepare matching:

cd data/Matching  
python extract_feature.py  
python match_5_split.py  

Train UODTN:

python train_UODTN.py  

Citation

Please cite our paper:
@inproceedings{zhuo2019unsupervised,
title={Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization},
author={Zhuo, Junbao and Wang, Shuhui and Cui, Shuhao and Huang, Qingming},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={750--759},
year={2019}
}

Acknowledgements: Our codes are mainly based on https://github.com/cyvius96/adgpm and https://github.com/thuml/Xlearn/tree/master/pytorch