Cross-modal hashing has attracted much attention in the large-scale multimedia search area. In many real applications, labels of samples have hierarchical structure which also contains much useful information for learning. However, most existing methods are originally designed for non-hierarchical labeled data and thus fail to exploit the rich information of the label hierarchy. In this paper, we propose an effective cross-modal hashing method, named Supervised Hierarchical Deep Cross-modal Hashing, SHDCH for short, to learn hash codes by explicitly delving into the hierarchical labels. Specifically, both the similarity at each layer of the label hierarchy and the relatedness across different layers are implanted into the hash-code learning. Besides, an iterative optimization algorithm is proposed to directly learn the discrete hash codes instead of relaxing the binary constraints. We conducted extensive experiments on two real-world datasets and the experimental results show the superior performance of SHDCH over several state-of-the-art methods. CCS CONCEPTS • Information systems → Multimedia and multimodal retrieval; • Computing methodologies → Visual content-based indexing and retrieval; Learning paradigms.
Hashing methods for cross-modal retrieval have recently been widely investigated due to the explosive growth of multimedia data. Generally, real-world data is imperfect and has more or less redundancy, making cross-modal retrieval task challenging. However, most existing cross-modal hashing methods fail to deal with the redundancy, leading to unsatisfactory performance on such data. In this paper, to address this issue, we propose a novel crossmodal hashing method, namely aTtEntion-Aware deep Cross-modal Hashing (TEACH). It could perform feature learning and hash-code learning simultaneously. Besides, with designed attention modules for different modalities, one for each, TEACH can effectively highlight the useful information of data while suppressing the redundant information. Extensive experiments on benchmark datasets demonstrate that our method outperforms some state-of-the-art hashing methods in cross-modal retrieval tasks. CCS CONCEPTS• Information systems → Multimedia and multimodal retrieval; • Computing methodologies → Visual content-based indexing and retrieval; Learning paradigms.
No abstract
With the vigorous development of multimedia equipments and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic. Thereinto, hashing has become a prevalent choice due to its retrieval efficiency and low storage cost. Although multi-modal hashing has drawn lots of attention in recent years, there still remain some problems. The first point is that existing methods are mainly designed in batch mode and not able to efficiently handle streaming multi-modal data. The second point is that all existing online multi-modal hashing methods fail to effectively handle unseen new classes which come continuously with streaming data chunks. In this paper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS). We design novel semantic-enhanced representation for data, which could help handle the new coming classes, and thereby construct the enhanced semantic objective function. An efficient and effective discrete online optimization algorithm is further proposed for OASIS. Extensive experiments show that our method can exceed the state-of-the-art models. For good reproducibility and benefiting the community, our code and data are already publicly available.
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