  In frame-based vision, object detection faces substantial performance
degradation under challenging conditions due to the limited sensing capability
of conventional cameras. Event cameras output sparse and asynchronous events,
providing a potential solution to solve these problems. However, effectively
fusing two heterogeneous modalities remains an open issue. In this work, we
propose a novel hierarchical feature refinement network for event-frame fusion.
The core concept is the design of the coarse-to-fine fusion module, denoted as
the cross-modality adaptive feature refinement (CAFR) module. In the initial
phase, the bidirectional cross-modality interaction (BCI) part facilitates
information bridging from two distinct sources. Subsequently, the features are
further refined by aligning the channel-level mean and variance in the two-fold
adaptive feature refinement (TAFR) part. We conducted extensive experiments on
two benchmarks: the low-resolution PKU-DDD17-Car dataset and the
high-resolution DSEC dataset. Experimental results show that our method
surpasses the state-of-the-art by an impressive margin of $\textbf{8.0}\%$ on
the DSEC dataset. Besides, our method exhibits significantly better robustness
(\textbf{69.5}\% versus \textbf{38.7}\%) when introducing 15 different
corruption types to the frame images. The code can be found at the link
(https://github.com/HuCaoFighting/FRN).
