Paper Info
Title
Uncertainty-Aware and Multigranularity Consistent Constrained Model for Semi-Supervised Hashing
Abstract
Recently, deep semi-supervised hashing methods have attracted increasing attention, which can significantly improve retrieval performance by leveraging abundant unlabeled data. These methods usually generate surrogate supervision signals to learn with unlabeled data, such as neighborhood information and augmentation invariant requirements. However, an essential issue of these methods is that the supervised signals are not always reliable, which may damage the performance. In this paper, we propose a novel Uncertainty-Aware and Multi-Granularity Consistent Constrained Semi-Supervised Hashing (UMCSH) method to alleviate the negative effects of noisy supervised signals and enlarge the inter-class distance. Specifically, our UMCSH mainly consists of an Uncertainty-Aware Instance-Level Consistency (UAILC) model and a Cluster-Based Class-Level Consistency (CBCLC) model. UAILC introduces an uncertainty estimation method to select reliable supervised signals to extract discriminative features for each unlabeled data. CBCLC establishes connections between labeled data and unlabeled data by encouraging each unlabeled sample to be close to the hash center (calculated with the labeled data) according to its pseudo-label. Extensive experimental results demonstrate the superior performance of our proposed approach compared with several state-of-the-art semi-supervised hashing methods.
Year
DOI
Venue
2022
10.1109/TCSVT.2022.3174577
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
DocType
Volume
Semi-supervised deep hashing,large scale image retrieval,uncertainty estimation,consistent constraint
Journal
32
Issue
ISSN
Citations 
10
1051-8215
0
PageRank 
References 
Authors
0.34
28
7
Name
Order
Citations
PageRank
Shuai Cheng100.68
Yucan Zhou2508.08
Wanqian Zhang354.11
Dayan Wu4157.33
Chule Yang500.34
Bo Li657845.93
Wang Weiping733563.84