Graph Transformer for 3D point clouds classification and semantic segmentation
W Zhou, Q Wang, W Jin, X Shi, Y He - Computers & Graphics, 2024 - Elsevier
Recently, graph-based and Transformer-based deep learning have demonstrated excellent
performances on various point cloud tasks. Most of the existing graph-based methods rely …
performances on various point cloud tasks. Most of the existing graph-based methods rely …
Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation
Spiking neural networks (SNNs) have received widespread attention as an ultra-low power
computing paradigm. Recent studies have focused on improving the feature extraction …
computing paradigm. Recent studies have focused on improving the feature extraction …
[HTML][HTML] Spiking PointCNN: An Efficient Converted Spiking Neural Network under a Flexible Framework
Spiking neural networks (SNNs) are generating wide attention due to their brain-like
simulation capabilities and low energy consumption. Converting artificial neural networks …
simulation capabilities and low energy consumption. Converting artificial neural networks …
Efficient 3D Recognition with Event-driven Spike Sparse Convolution
Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-
temporal features. Point clouds are sparse 3D spatial data, which suggests that SNNs …
temporal features. Point clouds are sparse 3D spatial data, which suggests that SNNs …