Results 51 to 60 of about 407 (177)
Unsupervised Deep Relative Neighbor Relationship Preserving Cross-Modal Hashing
Mathematics, 2022 The image-text cross-modal retrieval task, which aims to retrieve the relevant image from text and vice versa, is now attracting widespread attention.
Xiaohan Yang +5 more
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Data‐Driven Engineering of Thermostable Collagen‐Mimetic Peptoid Triple Helices
Macromolecular Rapid Communications, EarlyView.Data‐driven active learning discovery of collagen‐mimetic peptoids capable of forming a thermostable collagen‐like triple helix using computational screening followed by experimental validation. ABSTRACT Collagen‐mimetic peptides (CMPs) are engineered molecules designed to replicate the triple‐helical structure of natural collagen.
Alex Berlaga +6 more
wiley +1 more source
Deep Self-Supervised Hashing With Fine-Grained Similarity Mining for Cross-Modal Retrieval
IEEE AccessWith the efficiency of storage and retrieval speed, the hashing methods have attracted a lot of attention for cross-modal retrieval applications. In contrast to traditional cross-modal hashing by using handcrafted features, deep cross-modal hashing ...
Lijun Han +5 more
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IEEE Access, 2023 Supervised cross-modal retrieval has significant advantages in retrieval efficiency and storage cost. In the field of hashing retrieval, existing supervised methods are divided into single-label and multi-label methods.
Mingyong Li, Jiabao Fan, Ziyong Lin
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Quarterly Journal of the Royal Meteorological Society, EarlyView.Key Findings: An assimilation methodology is established for the Tomorrow.io microwave sounder (TMS) flying on CubeSats in sun‐synchronous and inclined orbits, and in all cloud scenes. The TMS has a significant impact on weather forecast lead times up to 3 days in the Tropics in a research‐quality numerical weather prediction setting, and yields water ...
Jonathan J. Guerrette +3 more
wiley +1 more source
Applied SciencesThis paper introduces Graph-Propagated Multi-Scale Hashing with Contrastive Learning (GPMCL), a novel unsupervised cross-modal hashing framework designed to address the semantic deficiency in large-scale unlabeled multimodal data.
Yan Zhao, Guohua Shi
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Deep Binary Reconstruction for Cross-Modal Hashing [PDF]
IEEE Transactions on Multimedia, 2017 8 pages, 5 figures, accepted by ACM Multimedia ...
Di Hu, Feiping Nie, Xuelong Li
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VIEW, EarlyView.Our study presents a tumour‐informed circulating tumour DNA (ctDNA) workflow designed to enhance the detection of recurrence in head and neck cancer patients, addressing key challenges such as low ctDNA tumour fractions and tumour heterogeneity. Abstract Circulating tumour DNA (ctDNA) is a promising minimally invasive biomarker for monitoring treatment
Xiaomin Huang +8 more
wiley +1 more source
Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval
Entropy, 2020 Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging.
Shuli Cheng, Liejun Wang, Anyu Du
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Cross-modal hashing with semantic deep embedding [PDF]
Neurocomputing, 2019 Cross-modal hashing has demonstrated advantages on fast retrieval tasks. It improves the quality of hash coding by exploiting semantic correlation across different modalities. In supervised cross-modal hashing, the learning of hash function replies on the quality of extracted features, for which deep learning models have been adopted to replace the ...
Yan, Cheng +4 more
openaire +4 more sources