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SOTA
异常检测
Anomaly Detection On Road Anomaly
Anomaly Detection On Road Anomaly
评估指标
AP
FPR95
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
FPR95
Paper Title
OodDINO
95.21
2.11
-
RbA
90.28
4.92
RbA: Segmenting Unknown Regions Rejected by All
DOoD
89.1
8.8
Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond
cDNP
85.6
9.8
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection
Mask2Anomaly
79.70
13.45
Unmasking Anomalies in Road-Scene Segmentation
RPL+CoroCL
71.61
17.74
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
PEBAL
45.10
44.58
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
Synboost
41.83
59.72
Pixel-wise Anomaly Detection in Complex Driving Scenes
SML
25.82
49.74
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
SynthCP
24.86
64.69
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
0 of 10 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
异常检测
Anomaly Detection On Road Anomaly
Anomaly Detection On Road Anomaly
评估指标
AP
FPR95
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
FPR95
Paper Title
OodDINO
95.21
2.11
-
RbA
90.28
4.92
RbA: Segmenting Unknown Regions Rejected by All
DOoD
89.1
8.8
Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond
cDNP
85.6
9.8
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection
Mask2Anomaly
79.70
13.45
Unmasking Anomalies in Road-Scene Segmentation
RPL+CoroCL
71.61
17.74
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
PEBAL
45.10
44.58
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
Synboost
41.83
59.72
Pixel-wise Anomaly Detection in Complex Driving Scenes
SML
25.82
49.74
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
SynthCP
24.86
64.69
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
0 of 10 row(s) selected.
Previous
Next
Anomaly Detection On Road Anomaly | SOTA | HyperAI超神经