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Deep Cross-Modal Hashing With Hashing Functions and Unified Hash Codes Jointly Learning [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2022 Due to their high retrieval efficiency and low storage cost, cross-modal hashing methods have attracted considerable attention. Generally, compared with shallow cross-modal hashing methods, deep cross-modal hashing methods can achieve a more satisfactory performance by integrating feature learning and hash codes optimizing into a same framework ...
Rong-Cheng Tu +6 more
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Self-Supervised Cluster-Contrast Distillation Hashing Network for Cross-Modal Retrieval
IEEE Access, 2023 Traditional cross-modal hash models enable efficient and fast retrieval between multimodal data by training high-quality hash representations. The key to the cross-modal hashing model is feature extraction.
Haoxuan Sun, Yudong Cao, Guangyuan Liu
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Ranking-Based Deep Cross-Modal Hashing
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process.
Liu, Xuanwu +5 more
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Deep Adversarial Cascaded Hashing for Cross-Modal Vessel Image Retrieval
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 In recent years, cross-modal remote sensing image retrieval has attracted a lot of attention in remote sensing (RS) information processing. It is worth mentioning that land cover scenes, whether unimodal or cross-modal, are the primary research contents ...
Jiaen Guo, Xin Guan
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Unsupervised Generative Adversarial Cross-Modal Hashing
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Cross-modal hashing aims to map heterogeneous multimedia data into a common Hamming space, which can realize fast and flexible retrieval across different modalities. Unsupervised cross-modal hashing is more flexible and applicable than supervised methods, since no intensive labeling work is involved.
Zhang, Jian, Peng, Yuxin, Yuan, Mingkuan
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Robust Supervised Deep Discrete Hashing for Cross-Modal Retrieval
TechnologiesThe exponential growth of multi-modal data in the real world poses significant challenges to efficient retrieval, and traditional single-modal methods are no longer suitable for the growth of multi-modal data.
Xiwei Dong +7 more
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Deep Lifelong Cross-Modal Hashing
IEEE Transactions on Circuits and Systems for Video TechnologyHashing methods have made significant progress in cross-modal retrieval tasks with fast query speed and low storage cost. Among them, deep learning-based hashing achieves better performance on large-scale data due to its excellent extraction and representation ability for nonlinear heterogeneous features. However, there are still two main challenges in
Liming Xu +4 more
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Feature Fusion-Based Cross-Modal Proxy Hashing Retrieval
Applied SciencesDue to its cost-effective and high-efficiency retrieval advantages, deep hashing has attracted extensive attention in the field of cross-modal retrieval.
Yan Zhao, Huaiying Li
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Supervised Intra- and Inter-Modality Similarity Preserving Hashing for Cross-Modal Retrieval
IEEE Access, 2018 Cross-modal hashing has drawn considerable interest in multimodal retrieval due to the explosive growth of big data on multimedia. However, the existing methods mainly focus on unified hash codes learning and investigate the local geometric structure in ...
Zhikui Chen +4 more
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A Cross-Modal Hash Retrieval Method with Fused Triples
Applied Sciences, 2023 Due to the fast retrieval speed and low storage cost, cross-modal hashing has become the primary method for cross-modal retrieval. Since the emergence of deep cross-modal hashing methods, cross-modal retrieval significantly improved.
Wenxiao Li +6 more
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