2. Related Work Online Multi-Object Tracking 运动和外观被证明是最有区分度的特征。 基于深度学习的运动特征提取: FANTrack: 3D Multi-Object Tracking with Feature Association Network. 2019 Frame-Wise Motion and Appearance for Real-time Multiple Object Tracking. BMVC, 2019. 2 Graph Neural Networks GNN...
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud Motivation CNN需要regular grids作为输入,但点云通常是sparse and irregular,因此采用规则的网络表示点云grid-based,会导致每个grid cell中点的分布不均,用CNN处理crowded cells会有潜在的信息损失,处理empty cells会浪费计算资源 Point-based...
A method for multi-object tracking includes receiving a sequence of images generated at respective times by one or more sensors configured to sense an environment through which objects are moving relative to the one or more sensors, and constructing a message passing graph in which each of a ...
原文标题：GraphFPN: Graph Feature Pyramid Network for Object Detection原文链接：https://arxiv.org/abs/2108.00580 一、摘要 图像语义理解往往需要多尺度特征，而特征金字塔在目标检测和语义分割等等问题中已经被证明十分有效。当前性能领先的多尺度特征学习方法主要是通过固定拓扑结构的神经网络去进行跨空间和跨尺度...
参考文献 [1] Geoffrey Hinton. How to represent part-whole hierarchies in a neural network. arXiv preprint arXiv:2102.12627, 2021. [2] Geoffrey Hinton. Some demonstrations of the effects of structural descriptions in mental imagery. Cognitive Science, 3(3):231–250, 1979. [3] Kevis-Kokitsi ...
A Fusion Method of3D Object Detection Graph Neural Network Based onLocal andGlobal Data Augmentation LiDAR-based 3D object detection is an important task for autonomous driving because it provides the location information of objects on the road. However, t... Y Zheng,X Liu,K Ruan,... 被引量...
graph neural networks 图神经网络 graph convolutional networks 图卷积神经网络 graph representation learning 图表示学习 graph autoencoder 图自动编码器 network embedding 网络嵌入 pattern recognition 模式识别 data mining 数据挖掘 object detection 目标检测 ...
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud, CVPR 2020. - WeijingShi/Point-GNN
Theforwardmethod defines the forward pass of a neural network. It essentially specifies how input data should be processed through the different layers of the network during training and inference to produce an output. Let’s walk through the different parts of the model architecture to...
GTA: Graph Truncated Attention for Retrosynthesis Cost-Aware Graph Generation: A Deep Bayesian Optimization Approach Modular Graph Transformer Networks for Multi-Label Image Classification PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection...