Shaohua Teng
Biographic Data
| ID | 10054073 |
|---|---|
| NAME | Shaohua Teng |
| GIVEN NAMES | Shaohua |
| FAMILY NAME | Teng |
| SIGNATURE | TENG S |
| AFFILIATIONS | Guangdong University of Technology |
| ORCID | 0000-0002-7204-1288 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Robust Asymmetric Cross-Modal Hashing Retrieval With Dual Semantic Enhancement
As social media faces with large amounts of data and multimodal properties, cross-modal hashing (CMH) retrieval gains extensive applications with its high efficiency and low storage consumption. However, there are two issues that hinder the performance of the existing semantics-learning-based CMH methods: 1) there exist some nonlinear relationships, noises, and outliers in the data, which may degrade the learning effectiveness of a model; and 2) …
Kernel-Based Sparse Representation Learning With Global and Local Low-Rank Label Constraint
Due to the large-scale and multiscale natures of social media data, sparse representation (SR) learning methods are widely followed. However, there are three problems associated with the existing SR methods: 1) they neglect the fact that the semantic features of data may change during iterative learning, which leads to weak semantic learning; 2) they often assume that the data are linearly separable, while the data might be nonlinear in many real…
Fast Asymmetric and Discrete Cross-Modal Hashing With Semantic Consistency
Hashing has attracted widespread attention in the field of supervised cross-modal retrieval due to its advantages in search and storage. However, there are still some issues to be addressed, e.g.: 1) how to effectively combine sample and label semantics to learn hash codes; 2) how to reduce high computational requirements brought by computing a pairwise similarity matrix; and 3) how to effectively solve discrete optimization problems. To cope wit…
No prominent works on this page.
Fast Asymmetric and Discrete Cross-Modal Hashing With Semantic Consistency
Hashing has attracted widespread attention in the field of supervised cross-modal retrieval due to its advantages in search and storage. However, there are still some issues to be addressed, e.g.: 1) how to effectively combine sample and label semantics to learn hash codes; 2) how to reduce high computational requirements brought by computing a pairwise similarity matrix; and 3) how to effectively solve discrete optimization problems. To cope wit…
Robust Asymmetric Cross-Modal Hashing Retrieval With Dual Semantic Enhancement
As social media faces with large amounts of data and multimodal properties, cross-modal hashing (CMH) retrieval gains extensive applications with its high efficiency and low storage consumption. However, there are two issues that hinder the performance of the existing semantics-learning-based CMH methods: 1) there exist some nonlinear relationships, noises, and outliers in the data, which may degrade the learning effectiveness of a model; and 2) …
Kernel-Based Sparse Representation Learning With Global and Local Low-Rank Label Constraint
Due to the large-scale and multiscale natures of social media data, sparse representation (SR) learning methods are widely followed. However, there are three problems associated with the existing SR methods: 1) they neglect the fact that the semantic features of data may change during iterative learning, which leads to weak semantic learning; 2) they often assume that the data are linearly separable, while the data might be nonlinear in many real…
Artificial Intelligence (3 works) · Computer Science (3 works) · Theoretical Computer Science (3 works) · Advanced Image and Video Retrieval Techniques (2 works) · Double hashing (2 works) · Hash function (2 works) · Hash table (2 works) · Machine learning (2 works) · Video Surveillance and Tracking Methods (2 works) · Algorithm (1 work)