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SOTA
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Drone Navigation On University 1652 1
Drone Navigation On University 1652 1
评估指标
AP
Recall@1
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
AP
Recall@1
Paper Title
FSRA
81.53
87.87
A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
LPN + USAM
75.96
86.59
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
LPN
74.79
86.45
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
SAFA + USAM
71.77
83.23
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
RK-Net
65.76
80.17
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
Instance Loss
58.74
71.18
University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
0 of 6 row(s) selected.
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Next
HyperAI
HyperAI超神经
首页
算力平台
文档
资讯
论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
服务条款
隐私政策
中文
HyperAI
HyperAI超神经
Toggle Sidebar
全站搜索…
⌘
K
Command Palette
Search for a command to run...
算力平台
首页
SOTA
无人机导航
Drone Navigation On University 1652 1
Drone Navigation On University 1652 1
评估指标
AP
Recall@1
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
AP
Recall@1
Paper Title
FSRA
81.53
87.87
A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
LPN + USAM
75.96
86.59
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
LPN
74.79
86.45
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
SAFA + USAM
71.77
83.23
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
RK-Net
65.76
80.17
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
Instance Loss
58.74
71.18
University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
0 of 6 row(s) selected.
Previous
Next