Utilizing a graph neural network on the raw radar tensor we gain a significant improvement of +10% in average precision over a grid-based convolutional baseline network. The performance of both ...

One promising approach for analyzing event data is through graph convolutional networks (GCNs). However, current research in this domain primarily focuses on optimizing computational costs, neglecting ...

Keywords: EEG, driving fatigue detection, channel attention mechanism, graph convolutional network, spatial attention mechanism. Citation: Liu H, Liu Q, Cai M, Chen K, Ma L, Meng W, Zhou Z and Ai Q ...

This is the official PyTorch implementation of our paper: "Joint Object Detection and Multi-Object Tracking with Graph Neural Networks". Our project website and video demos are here. If you find our ...

Keywords: medical images, convolutional neural network, object detection, semantic segmentation, analysis. Citation: Yang R and Yu Y (2021) Artificial Convolutional Neural Network in Object Detection ...

This repository is the official code implementation of the GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization on IEEE and on Arxiv. Link to access a ...

Object detection models are much more complex than image classification networks and require more memory. “We added computer vision support to Edge Impulse back in 2020, and we’ve seen a ...