Yu Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yu Liu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Dawei Yan
Northwestern Polytechnical University, Xi’an 710060, China
1 shared publication - Guoting Wei
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication - Haizhao Jing
School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
1 shared publication - Haokui Zhang
Northwestern Polytechnical University
1 shared publication - Lijie Tao
School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
1 shared publication - Xizhe Xue
Northwestern Polytechnical University
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
- 6 January 2025
- Published by MDPI AG in Remote Sensing
- Vol. 17 (1) , 162
Advancements in Vision–Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques
Recently, the remarkable success of ChatGPT has sparked a renewed wave of interest in artificial intelligence (AI), and the advancements in Vision–Language Models (VLMs) have pushed this enthusiasm to new heights. Differing from previous AI approaches that generally formulated different tasks as discriminative models, VLMs frame tasks as generative models and align language with visual information, enabling the handling of more challenging problems. The remote sensing (RS) field, a highly practical domain, has also embraced this new trend and introduced several VLM-based RS methods that have demonstrated promising performance and enormous potential. In this paper, we first review the fundamental theories related to VLM, then summarize the datasets constructed for VLMs in remote sensing and the various tasks they address. Finally, we categorize the improvement methods into three main parts according to the core components of VLMs and provide a detailed introduction and comparison of these methods.
- 6 January 2025
- Published by MDPI AG in Remote Sensing
- Vol. 17 (1) , 162
Top Cited PapersOpen AccessAdvancements in Vision–Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques
Recently, the remarkable success of ChatGPT has sparked a renewed wave of interest in artificial intelligence (AI), and the advancements in Vision–Language Models (VLMs) have pushed this enthusiasm to new heights. Differing from previous AI approaches that generally formulated different tasks as discriminative models, VLMs frame tasks as generative models and align language with visual information, enabling the handling of more challenging problems. The remote sensing (RS) field, a highly practical domain, has also embraced this new trend and introduced several VLM-based RS methods that have demonstrated promising performance and enormous potential. In this paper, we first review the fundamental theories related to VLM, then summarize the datasets constructed for VLMs in remote sensing and the various tasks they address. Finally, we categorize the improvement methods into three main parts according to the core components of VLMs and provide a detailed introduction and comparison of these methods.