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Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval [PDF]

Introduction

Backward-compatible training (BCT) aims to deploy a new model without the operation of "backfilling". We introduce AdvBCT, an Adversarial Backward-Compatible Training method with an elastic boundary constraint that takes both compatibility and discrimination into consideration. The codes for AdvBCT and the benchmark are all publicly available in this repo. Thanks to the work Hot-refresh. Some implementaions of our code are based on it.

Our paper has been accepted by CVPR2023.

Datasets

refer to Dataset.md.

Enviroments

conda create -n bct python=3.7
conda activate bct
#conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
conda install faiss-cpu
pip install -r requirements.txt

Train

Evaluation

evaluate datasets on 32G v100

mkdir -p output/final_model
bash scripts/test.sh landmark roxford5k ./data/ROxfordParis/
bash scripts/test.sh landmark rparis6k ./data/ROxfordParis/
bash scripts/test.sh landmark gldv2 ./data/GLDv2 # take a long time

Results

alt results

Next-step

The following content will also be released soon.

License

The code is released under MIT license.

MIT License

Copyright (c) 2022 AdvBCT

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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.