Inverse RL Scene Dynamics Learning for Nonlinear Predictive Control in Autonomous Vehicles [0.03%]
逆强化学习场景动力学预测在自动驾驶汽车中的非线性预测控制
Sorin M Grigorescu,Mihai V Zaha
Sorin M Grigorescu
This article introduces the deep learning-based nonlinear model predictive controller with scene dynamics (DL-NMPC-SD) method for autonomous navigation. DL-NMPC-SD uses an a priori nominal vehicle model in combination with a scene dynamics ...
Boundary-Based Active Domain Adaptation for Semantic Segmentation Under Adverse Conditions [0.03%]
基于边界的主动领域适应用于不利条件下的语义分割
Xianzhe Xu,Gary G Yen,Chaoqiang Zhao et al.
Xianzhe Xu et al.
Existing domain adaptation semantic segmentation (DASS) methods under adverse conditions often depend on pseudo-labels for network training. However, these pseudo-labels are frequently plagued by noise and bias toward high-confidence predic...
A UHD Aerial Photograph Categorization System by Learning a Noise-Tolerant Topology Kernel [0.03%]
通过学习噪声容许拓扑核的超高清航空照片分类系统
Luming Zhang,Guifeng Wang,Ming Chen et al.
Luming Zhang et al.
With thousands of observation satellites orbiting the Earth, massive-scale ultrahigh-definition (UHD) images are captured daily, covering vast areas of land, often extending across millions of square kilometers. These images commonly featur...
Zhiqiang Pan,Honghui Chen,Wanyu Chen et al.
Zhiqiang Pan et al.
Link prediction on temporal networks aims to predict the future edges by modeling the dynamic evolution involved in the graph data. Previous methods relying on the node/edge attributes or the distance on the graph structure are not practica...
Ensemble Denoising Autoencoders Based on Broad Learning System for Time-Series Anomaly Detection [0.03%]
基于广义学习系统的集成去噪自编码器的时间序列异常检测方法
Yuanxin Lin,Zhiwen Yu,Kaixiang Yang et al.
Yuanxin Lin et al.
Time-series anomaly detection has gained considerable prominence in numerous practical applications across various domains. Nonetheless, the scarcity of labels leads to the neglect of anomalous patterns in data, as well as the inherent comp...
Xijia Tang,Chao Xu,Hong Tao et al.
Xijia Tang et al.
Positive and unlabeled (PU) learning, which trains binary classifiers using only PU data, has gained vast attentions in recent years. Traditional PU learning often assumes that all the positive samples are labeled accurately. Nevertheless, ...
Renhao Huang,Hao Xue,Maurice Pagnucco et al.
Renhao Huang et al.
Vision-based trajectory prediction is an important task that supports safe and intelligent behaviors in autonomous systems. Many advanced approaches have been proposed over the years with improved spatial and temporal feature extraction. Ho...
SDSimPoint: Shallow-Deep Similarity Learning for Few-Shot Point Cloud Semantic Segmentation [0.03%]
SDSimPoint：浅深相似性学习在少样本点云语义分割中的应用
Jiahui Wang,Haiyue Zhu,Haoren Guo et al.
Jiahui Wang et al.
Three-dimensional point cloud semantic segmentation is a fundamental task in computer vision. As the fully supervised approaches suffer from the generalization issue with limited data, few-shot point cloud segmentation models have been prop...
Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection [0.03%]
平均剪枝：提高分布外检测的性能和稳定性
Zhen Cheng,Fei Zhu,Xu-Yao Zhang et al.
Zhen Cheng et al.
Detecting out-of-distribution (OOD) inputs has been a critical issue for neural networks in the open world. However, the unstable behavior of OOD detection along the optimization trajectory during training has not been explored clearly. In ...
Yanbei Liu,Yu Zhao,Zhitao Xiao et al.
Yanbei Liu et al.
Graph contrastive learning (GCL), as a typical self-supervised learning paradigm, has been able to achieve promising performance without labels and gradually attracts much attention. Graph-level method aims to learn representations of each ...