计算机科学
竞赛
高斯分布
变压器
任务(项目管理)
人工智能
数据挖掘
情报检索
工程类
电气工程
政治学
量子力学
物理
电压
法学
系统工程
作者
Jiarui Hu,Zijun Huang,Fei Shen,Dian He,Qingyu Xian
标识
DOI:10.1109/igarss52108.2023.10283210
摘要
In this article, we introduce the method we used in the 2023 IEEE GRSS Data Fusion Contest Track 1. The task demands a fine-grained classification method for semantic urban reconstruction. Our experiments are based on Swin transformer, combined with Double-Head module and RFLA (Gaussian Receptive Field based Lable Assignment) strategy, which can effectively improve model's performance on small objects. Experimental results show that our method can bring significant improvement. We achieved the 4 th place in the final leader board.
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