Urban socio-semantic segmentation leverages digital and satellite imagery to provide critical spatial semantic information for downstream applications such as urban resource allocation. Although existing methods achieve high segmentation accuracy, they still suffer from inaccurate delineation of target boundaries. The...
Qi-Xiu Li, Zhong-Zhi He, Xiang Zhu et al.· 0 citations
This work introduces Slow-OCast, a transfer-learning based model designed for high-resolution ocean environmental forecasting that incorporates the slow-varying motion characteristics of the ocean and comprises two insightful modules.
Qi-Xiu Li, Xiang Zhu, Xiao-Yong Li et al.· Proceedings of the 32nd ACM...· 0 citations
Text summarization requires models to condense content while preserving key qualities such as consistency and coherence. Large language models (LLMs) have shown strong performance on this task and can be further improved through reinforcement learning (RL). However, most existing methods apply reward signals directly t...
Qi-Xiu Li, Chen-Long Bao, Xiang Zhu et al.· 0 citations
Regional high-resolution ocean environmental forecasting combines spatial numerical modeling with temporal prediction, and is essential for monitoring the ecological security of specific ocean regions. In recent years, deep learning methods are generally more computationally efficient than traditional numerical models...
Qixiu Li, Xiang Zhu, Xiaoyong Li et al.· Proceedings of the 32nd ACM...· 0 citations
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