  The audio-visual event localization task requires identifying concurrent
visual and auditory events from unconstrained videos within a network model,
locating them, and classifying their category. The efficient extraction and
integration of audio and visual modal information have always been challenging
in this field. In this paper, we introduce CACE-Net, which differs from most
existing methods that solely use audio signals to guide visual information. We
propose an audio-visual co-guidance attention mechanism that allows for
adaptive bi-directional cross-modal attentional guidance between audio and
visual information, thus reducing inconsistencies between modalities. Moreover,
we have observed that existing methods have difficulty distinguishing between
similar background and event and lack the fine-grained features for event
classification. Consequently, we employ background-event contrast enhancement
to increase the discrimination of fused feature and fine-tuned pre-trained
model to extract more refined and discernible features from complex multimodal
inputs. Specifically, we have enhanced the model's ability to discern subtle
differences between event and background and improved the accuracy of event
classification in our model. Experiments on the AVE dataset demonstrate that
CACE-Net sets a new benchmark in the audio-visual event localization task,
proving the effectiveness of our proposed methods in handling complex
multimodal learning and event localization in unconstrained videos. Code is
available at https://github.com/Brain-Cog-Lab/CACE-Net.
