On the Market-1501 dataset, we accelerate the re-ranking processing from 89.2s to 9.4ms with one K40m GPU, facilitating the real-time post-processing. Similarly, we observe that our method achieves comparable or even better retrieval results on the other four image retrieval benchmarks, i.e., VeRi-776, Oxford-5k, Paris-6k and University-1652, with limited time cost.
The paddlepaddle implementation can be found in [PaddlePaddle].
The pytorch version can be found in [Person_reID_baseline_pytorch].
@article{zhang2020understanding,
title={Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective},
author={Zhang, Xuanmeng and Jiang, Minyue and Zheng, Zhedong and Tan, Xiao and Ding, Errui and Yang, Yi},
journal={arXiv preprint arXiv:2012.07620},
year={2020}
}
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