Image segmentation using deep learning: A survey
Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …
applications such as scene understanding, medical image analysis, robotic perception …
A comprehensive survey on graph neural networks
Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …
from image classification and video processing to speech recognition and natural language …
Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds
Abstract We introduce Position Adaptive Convolution (PAConv), a generic convolution
operation for 3D point cloud processing. The key of PAConv is to construct the convolution …
operation for 3D point cloud processing. The key of PAConv is to construct the convolution …
Cylindrical and asymmetrical 3d convolution networks for lidar segmentation
State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the
point clouds to 2D space and then process them via 2D convolution. Although this …
point clouds to 2D space and then process them via 2D convolution. Although this …
EGNN: Graph structure learning based on evolutionary computation helps more in graph neural networks
In recent years, graph neural networks (GNNs) have been successfully applied in many
fields due to their characteristics of neighborhood aggregation and have achieved state-of …
fields due to their characteristics of neighborhood aggregation and have achieved state-of …
3D object detection for autonomous driving: A survey
Autonomous driving is regarded as one of the most promising remedies to shield human
beings from severe crashes. To this end, 3D object detection serves as the core basis of …
beings from severe crashes. To this end, 3D object detection serves as the core basis of …
GNN-based long and short term preference modeling for next-location prediction
Next-location prediction is a special task of the next POIs recommendation. Different from
general recommendation tasks, next-location prediction is highly context-dependent:(1) …
general recommendation tasks, next-location prediction is highly context-dependent:(1) …
Point-gnn: Graph neural network for 3d object detection in a point cloud
In this paper, we propose a graph neural network to detect objects from a LiDAR point cloud.
Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors …
Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors …
Graph neural networks for recommender system
Recently, graph neural network (GNN) has become the new state-of-the-art approach in
many recommendation problems, with its strong ability to handle structured data and to …
many recommendation problems, with its strong ability to handle structured data and to …
CMX: Cross-modal fusion for RGB-X semantic segmentation with transformers
Scene understanding based on image segmentation is a crucial component of autonomous
vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting …
vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting …