  Event cameras and RGB cameras exhibit complementary characteristics in
imaging: the former possesses high dynamic range (HDR) and high temporal
resolution, while the latter provides rich texture and color information. This
makes the integration of event cameras into middle- and high-level RGB-based
vision tasks highly promising. However, challenges arise in multi-modal fusion,
data annotation, and model architecture design. In this paper, we propose
EvPlug, which learns a plug-and-play event and image fusion module from the
supervision of the existing RGB-based model. The learned fusion module
integrates event streams with image features in the form of a plug-in, endowing
the RGB-based model to be robust to HDR and fast motion scenes while enabling
high temporal resolution inference. Our method only requires unlabeled
event-image pairs (no pixel-wise alignment required) and does not alter the
structure or weights of the RGB-based model. We demonstrate the superiority of
EvPlug in several vision tasks such as object detection, semantic segmentation,
and 3D hand pose estimation
