2021
DOI: 10.48550/arxiv.2103.12768
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DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation

Abstract: Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". The innovative way they acquire data presents several advantages over standard devices, especially in poor lighting and high-speed motion conditions. However, the novelty of these sensors results in the lack of a large amount of training data capable of fully unlocking their potential. The most common approach implemented by researchers to address this issue is to leverage simulated… Show more

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Cited by 1 publication
(5 citation statements)
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“…Discussion and Limitations. As it is currently not possible to fully replicate event camera behaviors, event simulation may create undesirable sim-to-real domain shift [78,90]. Nevertheless, several works showed that simulated events are robust enough to generalize well to real ones [33,78,90].…”
Section: Resultsmentioning
confidence: 99%
See 4 more Smart Citations
“…Discussion and Limitations. As it is currently not possible to fully replicate event camera behaviors, event simulation may create undesirable sim-to-real domain shift [78,90]. Nevertheless, several works showed that simulated events are robust enough to generalize well to real ones [33,78,90].…”
Section: Resultsmentioning
confidence: 99%
“…As it is currently not possible to fully replicate event camera behaviors, event simulation may create undesirable sim-to-real domain shift [78,90]. Nevertheless, several works showed that simulated events are robust enough to generalize well to real ones [33,78,90]. As we introduce event data in egocentric action recognition for the first time, we aim at providing a direct comparison with common benchmarks in the literature [14,15,27] and place the event modality in a competitive setting against well-established modalities.…”
Section: Resultsmentioning
confidence: 99%
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