1 code implementation • 17 Feb 2023 • Yukang Gan, Yixiao Ge, Chang Zhou, Shupeng Su, Zhouchuan Xu, Xuyuan Xu, Quanchao Hui, Xiang Chen, Yexin Wang, Ying Shan
To tackle the challenge, we propose a binary embedding-based retrieval (BEBR) engine equipped with a recurrent binarization algorithm that enables customized bits per dimension.
no code implementations • 13 Oct 2022 • Binjie Zhang, Shupeng Su, Yixiao Ge, Xuyuan Xu, Yexin Wang, Chun Yuan, Mike Zheng Shou, Ying Shan
The traditional model upgrading paradigm for retrieval requires recomputing all gallery embeddings before deploying the new model (dubbed as "backfilling"), which is quite expensive and time-consuming considering billions of instances in industrial applications.
1 code implementation • 29 Apr 2022 • Shupeng Su, Binjie Zhang, Yixiao Ge, Xuyuan Xu, Yexin Wang, Chun Yuan, Ying Shan
The task of privacy-preserving model upgrades in image retrieval desires to reap the benefits of rapidly evolving new models without accessing the raw gallery images.
2 code implementations • 3 Mar 2022 • Binjie Zhang, Yixiao Ge, Yantao Shen, Shupeng Su, Fanzi Wu, Chun Yuan, Xuyuan Xu, Yexin Wang, Ying Shan
The task of backward-compatible representation learning is therefore introduced to support backfill-free model upgrades, where the new query features are interoperable with the old gallery features.
1 code implementation • ICCV 2019 • Shupeng Su, Zhisheng Zhong, Chao Zhang
Deep cross-modal hashing further improves the retrieval performance as the deep neural networks can generate more semantic relevant features and hash codes.
2 code implementations • NeurIPS 2018 • Shupeng Su, Chao Zhang, Kai Han, Yonghong Tian
To convert the input into binary code, hashing algorithm has been widely used for approximate nearest neighbor search on large-scale image sets due to its computation and storage efficiency.