Luyao Teng
Biographic Data
| ID | 10054729 |
|---|---|
| NAME | Luyao Teng |
| GIVEN NAMES | Luyao |
| FAMILY NAME | Teng |
| SIGNATURE | TENG L |
| AFFILIATIONS | Guangzhou Panyu Polytechnic |
| ORCID | 0000-0002-3872-4085 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| 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…
No prominent works on this page.
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 (2 works) · Computer Science (2 works) · Machine learning (2 works) · Theoretical Computer Science (2 works) · Advanced Image and Video Retrieval Techniques (1 work) · Arithmetic (1 work) · Combinatorics (1 work) · Data mining (1 work) · Discrete mathematics (1 work) · Domain Adaptation and Few-Shot Learning (1 work)