Continuous cross-modal hashing
Generally, multimodal data with new classes arrive continuously in the real world. While advanced cross-modal hashing (CMH) focuses primarily on batch-based data with previously observed classes (ASCs), it disregards the effect of newly arriving classes (ANCs) on hash-code conflicts. In addition, class-level continuous hashing scenarios do not suit themselves well with the generic CMH configuration. To solve the aforementioned issues, we propose a novel framework, called CT-CMH, for the new task of continuous cross-modal hashing. For dealing with ANCs, CMH models require the ability of continuous learning, i.e. they can preserve the knowledge of previously observed data and, more crucially, they can be adapted to unseen data with ANCs. Specifically, we introduce the adaptive weight importance updating (AWIU) mechanism to alleviate the catastrophic forgetting problem of CMH and a new hash-code divergence (HCD) method to eliminate hash-code conflicts between ASCs and ANCs. When CT-CMH is equipped with both AWIU and HCD, it can consistently achieve high retrieval performance. The experiment results and visualization analyses validate the effectiveness of our approach. To the best of our knowledge, we are the first to introduce and implement the task of CCMH for ANCs.
First ; Corresponding
National Natural Science Foundation of China;
|WOS Research Area|
Computer Science ; Engineering
Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
|WOS Accession No|
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Computer Science and Engineering|
1.Department of Computer Science and Engineering,Southern University of Science and Technology,China
2.United Imaging Intelligence (UII) Co.,Ltd.,Beijing,China
3.School of Computer Science and Engineering,University of Electronic Science and Technology of China,China
4.School of Engineering Sciences,University of the Chinese Academy of Sciences,and the Peng Cheng Laboratory,China
5.Futurewei Technologies,Seattle,United States
|First Author Affilication||Department of Computer Science and Engineering|
|Corresponding Author Affilication||Department of Computer Science and Engineering|
|First Author's First Affilication||Department of Computer Science and Engineering|
Zheng，Hao,Wang，Jinbao,Zhen，Xiantong,et al. Continuous cross-modal hashing[J]. Pattern Recognition,2023,142.
Zheng，Hao.,Wang，Jinbao.,Zhen，Xiantong.,Song，Jingkuan.,Zheng，Feng.,...&Qi，Guo Jun.(2023).Continuous cross-modal hashing.Pattern Recognition,142.
Zheng，Hao,et al."Continuous cross-modal hashing".Pattern Recognition 142(2023).
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