Zobrazeno 1 - 10
of 4 867
pro vyhledávání: '"Li, Haifeng"'
Autor:
Xu, Linrui, Zhao, Ling, Guo, Wang, Li, Qiujun, Long, Kewang, Zou, Kaiqi, Wang, Yuhan, Li, Haifeng
The remote sensing image intelligence understanding model is undergoing a new profound paradigm shift which has been promoted by multi-modal large language model (MLLM), i.e. from the paradigm learning a domain model (LaDM) shifts to paradigm learnin
Externí odkaz:
http://arxiv.org/abs/2406.12479
Autor:
Li, Yansheng, Wang, Linlin, Wang, Tingzhu, Yang, Xue, Luo, Junwei, Wang, Qi, Deng, Youming, Wang, Wenbin, Sun, Xian, Li, Haifeng, Dang, Bo, Zhang, Yongjun, Yu, Yi, Yan, Junchi
Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting understanding of geospatial scenarios from perception to cognition. In SAI, objects exhibit great variations in scales and aspect ratios, and there exist rich relationships be
Externí odkaz:
http://arxiv.org/abs/2406.09410
The tokenizer, as one of the fundamental components of large models, has long been overlooked or even misunderstood in visual tasks. One key factor of the great comprehension power of the large language model is that natural language tokenizers utili
Externí odkaz:
http://arxiv.org/abs/2403.18593
Publikováno v:
Knowledge-Based Systems 2024
Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of current tr
Externí odkaz:
http://arxiv.org/abs/2403.16495
Deep neural network-based Synthetic Aperture Radar (SAR) target recognition models are susceptible to adversarial examples. Current adversarial example generation methods for SAR imagery primarily operate in the 2D digital domain, known as image adve
Externí odkaz:
http://arxiv.org/abs/2403.01210
In the later training stages, further improvement of the models ability to determine changes relies on how well the change detection (CD) model learns hard cases; however, there are two additional challenges to learning hard case samples: (1) change
Externí odkaz:
http://arxiv.org/abs/2402.16242
Autor:
Shao, Run, Yang, Cheng, Li, Qiujun, Zhu, Qing, Zhang, Yongjun, Li, YanSheng, Liu, Yu, Tang, Yong, Liu, Dapeng, Yang, Shizhong, Li, Haifeng
For a long time, due to the high heterogeneity in structure and semantics among various spatiotemporal modal data, the joint interpretation of multimodal spatiotemporal data has been an extremely challenging problem. The primary challenge resides in
Externí odkaz:
http://arxiv.org/abs/2401.00546
Image Quality, Uniformity and Computation Improvement of Compressive Light Field Displays with U-Net
We apply the U-Net model for compressive light field synthesis. Compared to methods based on stacked CNN and iterative algorithms, this method offers better image quality, uniformity and less computation.
Comment: 4 pages, 6 figures, conference
Comment: 4 pages, 6 figures, conference
Externí odkaz:
http://arxiv.org/abs/2312.16987
Ranker and retriever are two important components in dense passage retrieval. The retriever typically adopts a dual-encoder model, where queries and documents are separately input into two pre-trained models, and the vectors generated by the models a
Externí odkaz:
http://arxiv.org/abs/2312.16821
Publikováno v:
Information Science 2024
Local Attention-guided Message Passing Mechanism (LAMP) adopted in Graph Attention Networks (GATs) is designed to adaptively learn the importance of neighboring nodes for better local aggregation on the graph, which can bring the representations of s
Externí odkaz:
http://arxiv.org/abs/2312.08672