Discrete hashing with multiple supervision

Author: rjss

August undefined, 2024

WebMar 5, 2024 · NSDH: A Nonlinear Supervised Discrete Hashing framework for large-scale cross-modal retrieval Knowledge-Based Systems, Volume 217, 2024, Article 106818 Show abstract Research article Clustering-driven Deep Adversarial Hashing for scalable unsupervised cross-modal retrieval Neurocomputing, Volume 459, 2024, pp. 152-164 … WebAs an important branch of hashing methods, multi-view hashing takes advantages of multiple features from different views for binary hash learning. However, existing multi-view hashing methods are either based on shallow models which fail to fully capture the intrinsic correlations of heterogeneous views, or unsupervised deep models which suffer ...

Joint and Individual Feature Fusion Hashing for Multi-modal

WebTo remedy these problems, in this paper, we present a flexible two-step label embedding hashing method named L abel E mbedding S emantic- G uided H ashing ( LESGH ). In the first step, LESGH leverages an asymmetric discrete learning framework to learn discriminative compact hash codes only from label information, and adds the constraints … WebSpecifically, 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 … the project bcn

CVPR2024_玖138的博客-CSDN博客

WebMar 24, 2024 · To address the aforementioned issues, we propose a new model, known as supervised discrete multiple-length hashing (SDMLH), to simultaneously learn hash … WebDec 1, 2024 · An end-to-end deep hashing method called deep multiscale fusion hashing (DMFH) for cross-modal retrieval that can learn common hash codes directly without a relaxation, thereby avoiding a loss in accuracy during hash learning. 18 Multi-Task Consistency-Preserving Adversarial Hashing for Cross-Modal Retrieval WebFeb 27, 2024 · In light of the ability to enable efficient storage and fast query for big data, hashing techniques for cross-modal search have aroused extensive attention. Despite the great success achieved, unsupervised cross-modal hashing still suffers from lacking reliable similarity supervision and struggles with handling the heterogeneity issue between … signature car specialists reviews

Deep Discrete Cross-Modal Hashing with Multiple Supervision

Discrete Hashing With Multiple Supervision Semantic Scholar

WebOct 25, 2024 · To embedding the label supervision, Online Latent Semantic Hashing (OLSH) maps discrete tags to a continuous latent semantic space, while updating hash functions based on only newly arriving multimedia data points. This approach has produced promising retrieval performances to sequentially process the streaming data points, but … WebSep 23, 2024 · Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the... signature cassadou with glass lidWebMar 24, 2024 · To address the aforementioned issues, we propose a new model, known as supervised discrete multiple-length hashing (SDMLH), to simultaneously learn hash codes with multiple lengths. In this proposed SDMLH method, three types of information are respectively derived, from the hash codes with different lengths. The original … the project bedros

"WebJan 1, 2024 · Recently, discrete supervised hashing and deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a … " - Discrete hashing with multiple supervision

Discrete hashing with multiple supervision

Supervised Discrete Multiple-Length Hashing for Image …

WebSupervised cross-modal hashing has gained a lot of attention recently. However, most existing methods learn binary codes or hash functions in a batch-based scheme, which is inefficient in an online scenario, i.e., data points come in a streaming fashion.

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WebThus, we propose the Deep Discrete Cross-Modal Hashing with Multiple Supervision (DDCH ms) to further enhance the semantic consistency of heterogeneous modalities. It … WebSep 19, 2024 · Recently, supervised cross-modal hashing has attracted much attention and achieved promising performance. To learn hash functions and binary codes, most methods globally exploit the supervised information, for example, preserving an at-least-one pairwise similarity into hash codes or reconstructing the label matrix with binary codes.

http://www.nlm.medscape.idmu.unboundmedicine.unboundmedicine.com/medline/citation/30640611/Discrete_Hashing_with_Multiple_Supervision_ WebDec 27, 2024 · Unsupervised multi-modal hashing has received considerable attention in large-scale multimedia retrieval areas since its low storage and high search speed. Existing unsupervised multi-modal hashing methods usually aim to mine the complementary information and the structural information for different modalities and preserve them in …

WebOct 11, 2024 · During the unified hash codes learning, we design an efficient discrete optimization method based on column-sampling and this optimization method can … WebApr 19, 2024 · Unsupervised Generative Adversarial Cross-modal Hashing (UGACH) [31] proposed to use a generative adversarial network to learn better underlying features from multiple modalities.

WebMar 21, 2024 · To simultaneously support multiple multimedia retrieval tasks, multiple different hashing models should be equipped in a multimedia retrieval system. This …

WebJul 14, 2024 · Deep Discrete Cross-Modal Hashing with Multiple Supervision Neurocomputing, Volume 486, 2024, pp. 215-224 Show abstract Liuyin Lin received her B.S. degree in Computer Science and Technology from … signature car wash and detailing denver coWebDiscrete hashing with multiple supervision. X Luo, PF Zhang, Z Huang, L Nie, XS Xu. IEEE Transactions on Image Processing 28 (6), 2962-2975, 2024. 53: 2024: SCRATCH: A scalable discrete matrix factorization hashing for cross-modal retrieval. CX Li, ZD Chen, PF Zhang, X Luo, L Nie, W Zhang, XS Xu ... the project beat thatWebOct 15, 2024 · Supervised Discrete Hashing (SDH) ... J3 can use semantic supervision to direct how discrete hash codes are learned and add more semantic information to the hash codes that are created to improve hash code discrimination. 4. Experiments ... Hinton, G. Learning Multiple Layers of Features from Tiny Images. 2009. Available online: ... the project bedros keuilianWebDeep hashing has been widely used for large-scale cross-modal retrieval benefited from the low storage cost and fast search speed. However, most existing deep supervised methods only preserve the instance-pairwise relationship supervised by the semantic similarity matrix, which always inufficient heterogeneous correlation.Thus, we propose the Deep Discrete … signature catch pasteurized crab meat reviewsWebNov 17, 2024 · Hashing is an effective technique to solve large-scale data storage problem and achieve efficient retrieval, and it is also a core technology to promote the intelligent development of the new infrastructure construction. In most practical situations, label information is unavailable, and creating manual annotations is a time-consuming and … the project bibleWebMar 22, 2024 · Recently, supervised cross-modal hashing has attracted much attention and achieved promising performance. To learn hash functions and binary codes, most methods globally exploit the supervised... the project beautyWebSep 1, 2024 · In this paper, we develop a general deep supervised discrete hashing framework based on the assumption that the learned binary codes should be ideal for classification. Both the similarity... the project began