Effective sensor fusion with event-based sensors and deep network architectures

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (2016, methodology): “In previous work using event-based input data [21, 23], the timing information was sometimes removed and instead a frame-based representation was generated by computing the pixel-wise event-rate over some time period (as shown in Fig.”
Deep Learning in Spiking Neural Networks (2018, background): “Many studies have shown high performance of converted spiking CNNs (close to conventional CNNs) while using fewer operations and consuming less energy [177], [178], [141], [179], which enable the deep CNNs to be implemented on hardware [180], [181], [182].”
Event-Based Vision: A Survey (2019, methodology): “These datasets remain a good test for algorithm development, with many algorithms now achieving over 98 percent accuracy on the task [97], [126], [127], [209], [210], [211], but few would pro-pose digit recognition as a strength of event-based vision.”
Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks (2017, unknown): “Spiking CNN(pre-training*) (Neil and Liu, 2016) None 95.”
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks (2020, methodology): “Similar event-to-frame integrating methods for preprocessing neuromorphic datasets are widely used in both ANNs [23, 48, 47] and SNNs [64, 65, 8, 68, 23, 29, 38].”