Web: http://arxiv.org/abs/2203.17149

May 9, 2022, 1:10 a.m. | Simon Schaefer, Daniel Gehrig, Davide Scaramuzza

cs.CV updates on arXiv.org arxiv.org

The best performing learning algorithms devised for event cameras work by
first converting events into dense representations that are then processed
using standard CNNs. However, these steps discard both the sparsity and high
temporal resolution of events, leading to high computational burden and
latency. For this reason, recent works have adopted Graph Neural Networks
(GNNs), which process events as "static" spatio-temporal graphs, which are
inherently "sparse". We take this trend one step further by introducing
Asynchronous, Event-based Graph Neural Networks …

arxiv cv event graph graph neural networks networks neural neural networks

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