  Event cameras with high dynamic range ensure scene capture even in low-light
conditions. However, night events exhibit patterns different from those
captured during the day. This difference causes performance degradation when
applying night events to a model trained solely on day events. This limitation
persists due to a lack of annotated night events. To overcome the limitation,
we aim to alleviate data imbalance by translating annotated day data into night
events. However, generating events from different modalities challenges
reproducing their unique properties. Accordingly, we propose an unpaired
event-to-event day-to-night translation model that effectively learns to map
from one domain to another using Diffusion GAN. The proposed translation model
analyzes events in spatio-temporal dimension with wavelet decomposition and
disentangled convolution layers. We also propose a new temporal contrastive
learning with a novel shuffling and sampling strategy to regularize temporal
continuity. To validate the efficacy of the proposed methodology, we redesign
metrics for evaluating events translated in an unpaired setting, aligning them
with the event modality for the first time. Our framework shows the successful
day-to-night event translation while preserving the characteristics of events.
In addition, through our translation method, we facilitate event-based modes to
learn about night events by translating annotated day events into night events.
Our approach effectively mitigates the performance degradation of applying real
night events to downstream tasks. The code is available at
https://github.com/jeongyh98/UDNET.
