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 module. In the initial phase%2C the bidirectional cross-modality interaction (BCI) part facilitates information bridging from two distinct sources. Subsequently%2C 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 %24textbf{8.0}%25%24 on the DSEC dataset. Besides%2C our method exhibits significantly better robustness (textbf{69.5}%25 versus textbf{38.7}%25) when introducing 15 different corruption types to the frame images. The code can be found at the link (https:%2F%2Fgithub.com%2FHuCaoFighting%2FFRN).?quality=80&w=800)
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