Zobrazeno 1 - 10
of 1 386
pro vyhledávání: '"Zhang,Xueliang"'
Autor:
Li, Zhenshi, Muhtar, Dilxat, Gu, Feng, Zhang, Xueliang, Xiao, Pengfeng, He, Guangjun, Zhu, Xiaoxiang
Automatically and rapidly understanding Earth's surface is fundamental to our grasp of the living environment and informed decision-making. This underscores the need for a unified system with comprehensive capabilities in analyzing Earth's surface to
Externí odkaz:
http://arxiv.org/abs/2411.09301
Autor:
Muhtar, Dilxat, Shen, Yelong, Yang, Yaming, Liu, Xiaodong, Lu, Yadong, Liu, Jianfeng, Zhan, Yuefeng, Sun, Hao, Deng, Weiwei, Sun, Feng, Zhang, Xueliang, Gao, Jianfeng, Chen, Weizhu, Zhang, Qi
In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks directly from the given demonstrations without requiring gradient updates. While recent advances have expanded context windows to accommodate more demonstrations, thi
Externí odkaz:
http://arxiv.org/abs/2411.09289
Minimum Variance Distortionless Response (MVDR) is a classical adaptive beamformer that theoretically ensures the distortionless transmission of signals in the target direction, which makes it popular in real applications. Its noise reduction perform
Externí odkaz:
http://arxiv.org/abs/2409.06456
Music source separation (MSS) aims to separate mixed music into its distinct tracks, such as vocals, bass, drums, and more. MSS is considered to be a challenging audio separation task due to the complexity of music signals. Although the RNN and Trans
Externí odkaz:
http://arxiv.org/abs/2409.06245
Recent advancements in neural audio codec (NAC) unlock new potential in audio signal processing. Studies have increasingly explored leveraging the latent features of NAC for various speech signal processing tasks. This paper introduces the first appr
Externí odkaz:
http://arxiv.org/abs/2409.05784
In the context of global climate change and frequent extreme weather events, forecasting future geospatial vegetation states under these conditions is of significant importance. The vegetation change process is influenced by the complex interplay bet
Externí odkaz:
http://arxiv.org/abs/2407.12592
Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the growing size of very-high-resolution (VHR) remote sensing images poses challenges in effectively modeling context. While transformer-based models possess glob
Externí odkaz:
http://arxiv.org/abs/2404.02668
Speech enhancement aims to improve speech quality and intelligibility, especially in noisy environments where background noise degrades speech signals. Currently, deep learning methods achieve great success in speech enhancement, e.g. the representat
Externí odkaz:
http://arxiv.org/abs/2402.14225
Autor:
Feng, Pengming, Xie, Mingjie, Liu, Hongning, Zhao, Xuanjia, He, Guangjun, Zhang, Xueliang, Guan, Jian
Fine-grained ship instance segmentation in satellite images holds considerable significance for monitoring maritime activities at sea. However, existing datasets often suffer from the scarcity of fine-grained information or pixel-wise localization an
Externí odkaz:
http://arxiv.org/abs/2402.03708
The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains. In the remote sensing (RS) field, however, the divers
Externí odkaz:
http://arxiv.org/abs/2402.02544