.png){kind=icon}
返回
An Uncertainty-based Neural Network for Explainable Trajectory Segmentation
.png){kind=icon}
PRE
AI
DOI：10.1145/3467978.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As a variant task of time-series segmentation, trajectory segmentation is a key task in the applications of transportation pattern recognition and traffic analysis. However, segmenting trajectory is faced with challenges of implicit patterns and sparse results. Although deep neural networks have tremendous advantages in terms of high-level feature learning performance, deploying as a blackbox seriously limits the real-world applications. Providing explainable segmentations has significance for result evaluation and decision making. Thus, in this article, we address trajectory segmentation by proposing a Bayesian Encoder-Decoder Network (BED-Net) to provide accurate detection with explainability and references for the following active-learning procedures. BED-Net consists of a segmentation module based on Monte Carlo dropout and an explanation module based on uncertainty learning that provides results evaluation and visualization. Experimental results on both benchmark and real-world datasets indicate that BED-Net outperforms the rival methods and offers excellent explainability in the applications of trajectory segmentation.
Keyword:
Trajectory segmentation
time series
explainable neural network
uncertainty learning
期刊
IF:
6.6
论文数:
1.5K
被引数:
6.2K
机构
引用论文
Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling使用贝叶斯u-net从临床CT自动分割肌肉以进行个性化肌肉骨骼建模
OAAI
OAAI分享收藏Correcting System Matrices Using the Orthogonality Conditions of Distinct Measured Modes.png)
AIAA Journal
AIAA JournalIF0
PREAI
分享收藏分享收藏Feasibility of the modified 30-second sit-to-stand test in an isolation ward of moderate COVID-19
OAAI
OAAI分享收藏
查看更多内容